{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import lightgbm as lgb\n",
    "\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(731, 16)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant     dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1 2011-01-01       1   0     1        0        6           0   \n",
       "1        2 2011-01-02       1   0     1        0        0           0   \n",
       "2        3 2011-01-03       1   0     1        0        1           1   \n",
       "3        4 2011-01-04       1   0     1        0        2           1   \n",
       "4        5 2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### READ DATA ###\n",
    "\n",
    "df = pd.read_csv('day.csv')\n",
    "df['dteday'] = pd.to_datetime(df.dteday)\n",
    "\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>3065</td>\n",
       "      <td>677.402740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3410</td>\n",
       "      <td>1018.483607</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    min   max         mean\n",
       "yr                        \n",
       "0     9  3065   677.402740\n",
       "1     2  3410  1018.483607"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### CASUAL COUNT STATISTICS ###\n",
    "\n",
    "df.groupby('yr')['casual'].agg(['min','max','mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "### DEFINE CRPS METRIC ###\n",
    "\n",
    "def crps(y_true, y_pred):\n",
    "    y_true = np.clip(np.cumsum(y_true, axis=1), 0, 1)\n",
    "    y_pred = np.clip(np.cumsum(y_pred, axis=1), 0, 1)\n",
    "    return ((y_true-y_pred)**2).sum(axis=1).sum(axis=0) / (y_true.shape[1]*y_true.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['season', 'yr', 'mnth', 'holiday', 'weekday', 'workingday', 'weathersit', 'temp', 'atemp', 'hum', 'windspeed']\n"
     ]
    }
   ],
   "source": [
    "### DEFINE REGRESSOR COLUMNS ###\n",
    "\n",
    "columns = [c for c in df.columns if c not in ['instant','dteday','casual','registered','cnt']]\n",
    "print(columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(365, 16) (121, 16) (245, 16)\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2011-01-02</td>\n",
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       "      <td>670</td>\n",
       "      <td>801</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1349</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant     dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1 2011-01-01       1   0     1        0        6           0   \n",
       "1        2 2011-01-02       1   0     1        0        0           0   \n",
       "2        3 2011-01-03       1   0     1        0        1           1   \n",
       "3        4 2011-01-04       1   0     1        0        2           1   \n",
       "4        5 2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### TRAIN, VALIDATION, TEST SPLIT ###\n",
    "\n",
    "X_train = df[df.dteday < datetime(year=2012, month=1, day=1)].copy()\n",
    "train_date = X_train.dteday.values\n",
    "\n",
    "X_val = df[(df.dteday >= datetime(year=2012, month=1, day=1))&(df.dteday < datetime(year=2012, month=5, day=1))].copy()\n",
    "val_date = X_val.dteday.values\n",
    "\n",
    "X_test = df[(df.dteday >= datetime(year=2012, month=5, day=1))].copy()\n",
    "test_date = X_test.dteday.values\n",
    "\n",
    "print(X_train.shape, X_val.shape, X_test.shape)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1cdd4fed518>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D+F0AH+w9Xtdb5esArlFKfRjAycgaTN2ute4opfb1mlb9EMAbAXy8rnETQtqFXEeN+fGbENIqanRbtSEUdQdQIzSbo3kgdolYcXGBepzc5z0POOMM4K67qt82KYbWrMkdIuvWrcOWLVuwFMo0Z2ZmsG7duvgLe9R5ZF0L4HO9DskTAK7VWv+DUuo2ANcqpX4fwMMAXg0AWuu7lFLXArgbwDyAt2m9cHZ5C/pTCN3Q+0cIWQLIdRTPcYSQ5mhQ5NZ6KVYxsbiyKXLrqsm9++56tkuK0e3Wd4JmTW6U6elpnH766cMeRiup7ciqtf4xgLMdz+8G8GuedT4A4AOO5zcACNXzEkLGFDq5hJDGqbXxVMf98ygQiytLZ2XfcjJ+sCaXtJRGanIJIaQorMklhDSPOEh1XLzbTu4IMUwnl3c62wm7K5OWQpFLCGk1FLmEkMaRi+paGk+NsMhNrcmdnKzeyeVJoJ00MU8uRS4pAEUuIaTVMK5MCGmehmpy64hD10lqXHn16uqd3LrcQlKOJror8wYHKQBFLiGk1bDxFCGkeViT6yQ1rnzUUXRylwoykX0dd6Lp5JISUOQSQloNnVxCSOPoGmtyxyWu7BKxInLrcHIpctuJfC51uLlsPEVKQJFLCGk1rMklhDROnTW549x4yowrV+3kMq7cTpoQubzLTQpAkUsIaTUUuYSQ5mlynlwPR3YDncP+5cMgNa5MJ3fpQCeXtBSKXEJIq2FcmRDSPE3V5Hq2r7vAV08Avvfa6vdfhljjKRE6MzOsyV0q0MklLYUilxDSaujkEkIaZ9g1uU/clT1u+6fq91+G2BRCInSWL2d35Sb52teA++5rfr+xGu2y0MklJaDIJYS0GrlO4o1cQkhz1BhXTqnJ3X5L9njiL9Ww/xLE4sqyfPnyep1cnhAW86Y3AVdd1fx+zc+hjpsQsk1+3qQAFLmEkFZDJ5cQ0jh1Np5KcXJ33JI9rvx/qt9/GWJxZVPkal2t8DH3fbhltcrDZm6uHic1hvmZtLEm9yMfAW68sbrxkJGCIpcQ0moocgkhzVNnXDkyT67WwI7vLB5HW0h1cpctyx5t4XXLLcA73lFs36aIOniw2DbGlW53OCfJ2E2PqrZf1Mn94z8GzjuvuvGQkYIilxDSath4ihDSOLrOxlOGMHBtvzubdVa2X9sG8tTkul7zwhcCH/5wsQO6KagochfT6Qxf5LLxFGkZFLmEkFZDJ5cQ0jxDnEJo0fKWHfjyxJV9rwGA2dly+z50KP/640wbnNw2xpXJkoYilxDSath4ihDSPHXW5EbiyhgRkRtrPOV6jVLZ44ED+ffNuLIfOrmEDECRSwhpNXRyCSGNU+cUQrHuyouEbcsOfKnz5PpqcleuzB6LiFTGlf0My8mtewoh+T7xAoAUgCKXENJqKHIJIc0zxO7KbY4rx2pyY07uihXZYxEnlyLXjdbZPzq5hCyCIpcQ0mrYeIoQ0ji6qZpcl4huscjNG1f2ObmMK1fHMOtWWZNLWgxFLiGk1dDJJYQ0T53dlQ0x0B2zuHJZJ3f//uxfbN8UuX3aInLbOIUQWdJMDXsAhBASgo2nCCGNU2dNbjSu3HG/tg1oDUxOZq5dyhRCtvCJidw3vhGYmgKuvda/bYAi12SYdat0ckmLocglhLQaOrmEkOapsSY3T+OptoncbheYmMgeXaKmrJO7fXu/A7Nv2wBFrklbnFzW5JKWQZFLCGk1FLmEkMZprCZ3REXuxIRbeJStye10/GKJItfNODu57K5MSsCaXEJIq2HjKUJI89Qpcs04cqTxVBtrcicmMrfVJTzkOZlCyHZyY1MIdTrAkSP+ZQJFbp9hOrl1TyFEJ5eUgCKXENJq6OQSQhpHHNRaGk+NsJOrddjJLVuT2+36RW7MyT14EHj964GtW/3jH0fG2cmlyCUloMglhLQailxCSPPU6OSOek2uUv26XNdyoHhNbsjJjYncG24ArrkG+MM/9I9/HJG/Sx0iM3Xfde2/bpf6058GHn+8nm2ToUORSwhpNeyuTAhpnhobT8Wc3HGIK/uc3IneZWdI5M7O+pcJrvWf+tTs8ZFH3OuPK21pPDVqceVt24A3vQn42teq3zZpBRS5hJBWQyeXENI4tTae6rh/XnhuyE7uFVcAN93kXpbaeMpXkyvLy8aVXdMXTU9nj0tN5C6FuHId702+Q3WIc9IKKHIJIa2GjacIIc3TUE2ua/vDnif3ssuAF73IvUxqcn1ObqeTLRPBaQsIOZCXjSuHBPb27e71x5UqhOCWLcCznw08+mixfQP1dleu4wKAc/COPRS5hJBWQyeXENI4bZlCqI1xZanJ9QnNyUlgqjdDpc/JLdtdORSVXmpU4eT++MfAHXcA99yTbz3zOzBqTi5F7thDkUsIaTUUuYSQ5tH9x6rd1LbHlUOk1OROTJRzcufn4yK2yPJxpQqxtn9/9uiKgafsGygW+929O0sNPPZYePtFnNzYOhS5Yw9FLiGk1bDxFCGkeYwDTuVubo7GU20VuaEphCYm4k5uqCYXcDefSo0rA0srslyFk7tvX/boE7nz8+4uxGXjynffndV/b9rkXl5GiFLkLnkocgkhrYZOLiGkcWJ1s1Vte9ScXK3jUwhNTpZzcgF3ZDlPXPmhh9zbt7n+euDhh9Ne21aqEGsxkfu5zwFPf7r/pgVQTOSGbmqYy8s6uaE5nXlxMbZQ5BJCWg0bTxFCmqdGJ3fUa3JT4spFndyQyM3j5KaK3Ne+FrjqqrTXtpUm4srbtgF794ZFbpG4cqrILfLeYt246eSOPRS5hJBWQyeXENI4uk6Ra9bkuoRBxMmdPwRs/GNgbn+140ohZQqhiYnMzZXfTWJObmpcOebk7tzp3r7N7KxfYBXhla8Evvzl6raXQhNxZd8+qnJyY/stcpebInfJU5vIVUqdqpS6WSl1j1LqLqXUJb3n36uUelQptan378XGOpcrpTYrpe5TSp1nPP9cpdQdvWUfU0qpusZNCGkXFLmEkOYxRe6w4srKLXJ33w7c9xFg9w+qHVcKMSdXanInJvqvNxGxEuquDJSPK6cKrm632q7AN9wA3H57ddtLoQkn1ydyy3ZXbiquTJG7JKnTyZ0H8A6t9c8COBfA25RSZ/SWfURrvb7373oA6C27CMCZAM4HcKVSqncrEFcBuBjAM3v/zq9x3ISQFsHGU25e/nLg2muHPQpCxpSYEC2F6Ui6RG7vuYmpxa+1lw/joGjW5IamEPKJ3CriylNT8bhyqnDpdKoVuVWL5hSaajxl7ktIubGwf79/bMOMK7Mmd+ypTeRqrbdprX/U+3kfgHsAnBJY5UIAX9ZaH9Fa/xTAZgDPV0qtBXC01vo2rbUG8HkAL69r3ISQdkEn18311wM//OGwR0HIuGKIqGE1nlJTnsZT8twQRG5qTW6KkxtyY2Mit4ophLTO/lUpSjud5k9Ww3RyYzW5nQ5w1FHAm98c3q5vv1U5ua6x0ckdexqpyVVKnQbgbABySfaHSqkfK6U+q5Q6rvfcKQAeMVbb0nvulN7P9vOEkCUAG08NonV2TcBzMyE10VhNbgGR2y3p5H7lK8AVVxRbN7UmN+bkAsChQ4Prp8SVU0RuinCV8Vft5DZ9YG5zTa6cwD/9afd22XiK1EjtIlcptRrAVwFcqrV+Eln0+OkA1gPYBuBD8lLH6jrwvGtfFyulNiilNuxMbTpACGk1dHIH8SXHCCFV0VRNbqDx1MR0/+dF63cWvy4vr341cNllnrFFhLPWfRFbpiYXAA4fdq8PhBtPVRVXltdUdSDVejhx5bY4ua73bT4X+szqqMmlyF3y1CpylVLTyATuF7XWXwMArfV2rXVHa90FcDWA5/devgXAqcbq6wBs7T2/zvH8AFrrT2mtz9Fan7NmzZpq3wwhZChQ5A4i52v+TQipiVCkeHYvsON7JTaeGFee8Di5ddbkxg4q3W4WVQ7FlVNqcn37ajKuXLXIkc9jFOPKVdTkhiLBAPDAA/7lecV1CrHGU6zJHXvq7K6sAHwGwD1a6w8bz681XvYKAHf2fv46gIuUUsuVUqcjazB1u9Z6G4B9Sqlze9t8I4Dr6ho3IaRdMK48iNz0ppNLSF0EanIf+DTw7V8tXqtbWU1uDRfnKc5bmbiyuY7PCQaaiSvLa6o6kA5LNFWx37q6K5vP/cDRDZxOLqmRqRq3/QIAvwPgDqXUpt5zfwrgdUqp9cjOIA8CeDMAaK3vUkpdC+BuZJ2Z36b1wtH/LQD+BsAKADf0/hFClgB0LQeR6wH+TQipi0BN7vwBoDvXe77AZVSsJhcRkduUkzs3ByxbNri8zBRCMbe1THflmID2ba8qkVv19vLut401ueZzGzYAb3iDe/06anI5hdCSpzaRq7X+Htz1tNcH1vkAgA84nt8A4KzqRkcIGRXo5A4i52s6uYTUhA7U5C6IzIL/AVOd3NgUQmW7K8t0QCbmQeXIkUGRa9bklnVy7QOYdDsG4jW5VcaV6eTWJ3LN5a5po2Jx5aqcXHZXXpI00l2ZEEKKwprcQejkElI3ASG6IHIL/gc048iuyLNsP9pdueQBIORuAe7GUGVrckNuq/l72bjyUhK5ZcXa7Gz/u1B1Ta75+tBnxnlySQ1Q5BJCWk1RkfuFLwDnn1/9eNoAnVxCaiY0hVDZ7say/sSyYo2nqponNzSFD+CPDKc4ueIQ54krp+wbSOuunHJwHJe4clmxJi4uUK+TG4qn11GTy7jykocilxDSaorGlTduBG65pfLhtIK2O7kHD7Z3bISkEWg8VdbJNacIijWeqjOu7BK5MTfVFLlFanJDceWUfQPt7a48qk6uNJ0C6q3JDX1mdceVKXKXJBS5hJBWU7TxVKeTXSeNYy1vm7sr798PrFoF/PmfD3skhJQh4OQuxIVL1uT6nNxFIjjUeKrkxXkRJ1dqcmNxZXFy7QNwqpPrcvaqjitX3V15WKKp7H6rcnJjUwiFnF6fk1vnFEIUuWMPRS4hpNUUdXJDTTpHnTZ3nH700ezxy18e7jgIKcWi5lAVO7naFLEFphBaeK5mJzdUkxuLKwNutzdUkzvqceWqRXPe/Tbh5Lqahdmvca3nG19qTS6dXFIAilxCSKspWpMr51bXddqo02Ynd+/e7PG444Y7DkJKEarJFae1sMiVmtzpQQFtbjfWXblsTOXgwcHnUmtyQ05uSOSGhOiw4sp0cvs/xxpP1RVXZuMpUgMUuYQQ3HQT8NOfDnsUbihyB2lz46k9e7LHY48d7jgIKUdKTW7NceXoPLk1xJXz1OS6RLbU5ALVO7nD7q78ve8B99zjXz6qUwjVWZMb+0zqrMk11+EUQksSilxCCF73OuAjHxn2KNyUjSuPo8htc+Op3buzRzq5ZLSpoLvyju8BP/hPjoNX2bhyjY2nqqrJBeJOblviyqkH0je/GfjLv6xueyb33w9ceWX+9cruF+iL3NWrq6/JNT+HIjW5dTq5FLljD0UuIQT79rW3dtU8b+YRuuMsctvs5O7cmT3SySUjTRU1uTf9EvBv/x/QsQ5CugtA9URsgSmEytbkSlOottXkmr+7RM+w48qHDwMHDviXl6nJ/eIXgbe9rdi6ZZ1cOcmuXJm/JrcqJ5dTCJEaoMglZInT7WYC13UTtg2Y5yaK3Iw2O7m7dmWPq1cPdxyElCPByQ2J3K65ji30OoCaANSk9Tpr+7EphIrGlZcvzx7LzpPrOgDliSvboqjpuHJeUTo/Hz6hlBFNcgL2icwQZcWa7HvFiuZrcmV5Xgc5BdbkLnkocglZ4sg5u60i1xxXnnPRUhC5bXRyReTyuoGMNIsaT1kHx5QphB7fYKxvi+RuT+ROxRtP1RFXDoncWE1uSlw5tfFUXXHlqal6uivPz4cjT2VE0zBFrox7Zia/2IzVvQ6zuzKd3CUPRS4hSxy5zqHIHR3aPIWQiNy2fp8IScOM1RZwcrfdOPj6/gYB9JzcQo2nSlz4A+Wd3FhcOVSTmxpXDoncycmwYPI5vb7XV+XklokrxxzNlHWrcHLzzlebJ67MmlzSMBS5hCxxZBaJtoqS+fn+NRPjyhmj4OS2cWyEJLOoJrdA46lDj/Z/HhDJ3UzgTnhErtl4KhRXjjW+8lzP8vwAACAASURBVJHq5Ppqcss6ubI8FFd2iR5ZPkyRG3Jyy4imMiK3qrhyipObtyY31cmto7tyrClWFSL3n/8ZeNGLin1upHYocglZ4rTZyZ2dzc4/ck1GJzejzU6uNJ5q4/eJkHQCceWFeXID4igmklXAyU2NKxd1cpctyx7L1uQWnUJoaqq/rbz7npz071ueyxtXTj2Qpjq5ZUSuz9FMWbdsXLlMTe6yZdU3ntK6/5nWEVeuoib3da/L5mDcurX4NkhtUOQSssRps8jdvDl7fMYzskc6uRmj4OS28ftESDolG08hJHK7YZFrxpVddbdlnVyJxtRVkxubQkiWF4krx/YNDN/JbTqu3ETjqVhceXq6+prcULQ9hSbiyk8+mT3KjSPSKihyCRlzXvta4L/8F//yNovcu+/OHs88M3ukk5vR1u7K3W5/ntw2fp8ISSbUeCpF5AadXKnJnQK6RRpPlazJlYNjXTW5qU5uke7KIRfZFFzD6K5cZ+Mprf3jbMLJTRG5ZWpyQ04r4P+ub9oEfPvb7mVNNJ7at6/8NkhtTA17AISQern77vDUfm0XuUoBZ5yR/U6Rm9HWeXKfeKJc7xVC2kNApHYTnNRF4tQWep2sHjcWV/Y2nirZXVkOpGVqcn1TCKXU5KbElX3z5EpcOebk1tFdudOpbwqhmJP7y78M3HEHsGdPtfsF8jm59t9KhKRP5JZxcmNRZwA4++zF4/CtX5fILePek9qhk0vImNPthgWsNJ5q4zH67ruB004DVq3KfmdcOaOtcWVxcYF23jQhJJlFUUmfkxuqye24f842iGB35UXz5OrBA1+Kkzx/ANh8dfjiv0xNri8ynFKTG4srT0y0L64sTmrKFEKu7ckd23vvDa/rE5nf/S6wd2/YDS3r5C5fXrwmNxZX9n0moVpk8/VlG0/VPU8undxWQpFLyJjT6YTLfNrs5N5zT+biyjUTndyMtjaeMq//2vh9IiSdkjW55jJnd+XExlP2WLINep43ePQbwO0XA/vuH1yW6uSGanKLTiGU4uSuWNFMXDmPyE05oYTE5t//ffb4qU+Ftx+ryb3vPv+6ZZzcycns71ZXXNn3mYTiyilOrj0+k9gcvlU4uaH9k6FDkUvImBNzctsqcufns/M5Re4gbXVyzc+nbd8nQnJRtiY3tfGUM/IsInfavZ+UuHK3JxIHOkOjf+CQGI9rGRCuyU2ZQsj1mpSa3FCn3tS4ch6Rm/Ja08n0HdxCYvP007PH+x03Hcx1fSJzzZrscePGwWVVxJWnpuoRuaaTG5sn175xkcfJDcW4gfrnyW3byZgAoMglZOzpdkfTyX3ssewa6xnPyK6VAMaVhbY2njLP8237PhGSj24vLozyTq5zCqHJTOhG58l17Kebsv/Aa6qqyS3beMonmEIiNzWuXHVNrnlA851UQnHl6d4Ni5/8JLx93xRCz3xm9vijH/n3WyaunOrk+ubJjU0hFHNyXY218ji50tbfpInGUwJFbiuhyCVkzBnVuLKc62dm6OTatLXxlDmeto2NkFxo3Y8LO2tqXc+b6wdE7qKaXMdBzRa5rsZVMkbv/guK3Kprcl3unK8mV/YdcgVT4sqpTm6eLnnmCdJXlxsSTbIPmRfPt9x3spY7vS6RW7Z2tSonN1STGxO5wKDAT+muLJgNIVzbrkPklp3iiNQORS4hY05q46m2iVw5v01OlnNyQ31CRpW2OrmMK5PxQfud1KR5alPiyh4ndyGu7Nl/Sk1uqDlWmSmEzJpcn2gJ1eSmxJVDgigWV1Yqe03VNbl5nFzXvs24cyjWGxOZrrhzrINxjE4nLnJ9jadSuyuniFx73ylO7tFHZ49FRG5ZB9z8/8O7uq2EIpeQMWdUnVyzlKeIkyuvpZPbHIwrk/FB++PKeeLCrvV1txdXTnRyfSI7af8l4sq+jrdl5slNaTxVJq4sArzquHJM/Me2Zz730EP+5TGRG2qgZP+citl4yjcfbxWNp2Lz6NrftxSH+thjs0eXyK07rmzWAbftZEwAUOQSMvaMcuMpoH/jHmBcWaCTS0jN6G42ly0QqMlNjSs7RKqaABCpyZ2IxJWDjadKxpUnJ8MitswUQrGa3DJx5YmJdjq55j5c0wilityYUCwitkwn1zeGWE2urw66TFw5RbyHRG7dceW9e/s/U+S2EopcQsacUW08VVVceRxFbtud3FQjhZD2ogGoXqS4iJOaGld2Obm913ud3IS48kLdcEEn11fXmjKFUMzJlThzHXHlUJTa9XrzMUTZmlxz/Sef9C/3naxlm6G6V9++Y5hOrm8MdU8h5NpvipO7alX2GHNy65hCyBS5bbvjTABQ5BIy9sTiylKT2zZRIuekonHlcRa5bXdyly9v300TQnKhReQ65rItK3LNxlOuOHGVcWXXa0IHxxShGYsMp9bk5o0ri5M7jLhy2e7Ksa58VTm5ZWtyfWPw1eSmuO+A/6aJuU6RuLK8Zhg1uYwrtx6KXELGnFGNK9PJ9dPWeXLNa9S2fZ8IyYfuzQcbELmlGk8FphBCFwsCW17v3H9C46lQTW5IME1N+Z3aumpyU1zBmIssY0sRLk12VzbXr1rkttnJraq7su99yWtiIjf0XWdceWyhyCVkzBnVxlN0cv3I59lWJ5cil4w82nBbvU5qak2uQySryBRCEmcG4K3JLTuFUBHnLRYJTqnJLTqFUGpcue01uaG/u2+e3LobTxWtyY11V87TeKpIXDkkcmNT/LDx1NhDkUvImDMOTi5F7mLo5BJSNz0n19kcKlDvurB6x/2zrBeaQsgWud6a3JJx5SI1lFKTW0Vc2VeTu2xZWED79m3WC9cZVy7bXdm1PFaTW6eTm6fxVJNObop4T4kr+5pisSZ37KHIJWTMGVUnl3FlP21tPGXW5LZtbITkI1CTmzSFUBeY6IkGl8j1ucTZDnrLfSI3wcmVMeaNK+dxctsYV26i8VSZeXLN17nWjTWe0tr/d/PtO0Ysrmzu07fv0PcFKC9yyzi5sXrgogJ1377BbZFWQZFLyJjT7aY1nmqbyGVc2U9bG0/RySVjgzSemigYV0YXUCJyHeurSQSnEJIphmRbrv0XcXK17l/8d7uD4iFFtCgVb/4EVN94qq64svk38WGOx+fkVhFXjjm5rvXrbjwVcopjbmkeJzcUV47V5JqCM3VsZZ3c2GdKhg5FLiFjzjjElenkLqatcWXTyW3b94mQfBhCs0jjKd3tz3Prijvnqcn1xZWTanI9NZSxuthQ46mQmxqryTWd3NAUQi7hWVd3ZddYbFKc3DJx5TIit+7GUykCu4qa3DLdlUMCOuYyFxW5ZW8ukNqhyCVkzOl0suOv7xjcVpFLJ9dPWxtP0cklY0MVUwiF4srBmtwOkuLKKd2V7XVNIWn+LjRZkxuKK/uWVxlXzuPEVRlXDjm9o+rkFp1CKCRy83RXdu3bjCvX4eTmuUlChgJFLiFjjhyHfaKjrSKXjaf8tNXJjaUNCRkdjCmEvN2NI3FhFRO5gXlyU+LKRebJjQnJFFFSdgqhmIu8bNni3+11Y05u3riya182ZacQKtt4ytymfbKu28kNCfRUkeu7aWKuU6a7cszJZVx5SVKbyFVKnaqUulkpdY9S6i6l1CW955+ilPqmUur+3uNxxjqXK6U2K6XuU0qdZzz/XKXUHb1lH1NKwouEkBBm4st37pSaXKBdzmCVceU8640CbXVyGVcmY8OCkxuIK8emEPI6uZ34PLmyXLbl2n+ReXJtketzBeucQiglrmyOxd52rCY3b+Mp11hs8ji5ZeLKsSmEXOuXdXJjUwiFRLQZfy/SrTs1rlzWyQ0JbDq5Y0udTu48gHdorX8WwLkA3qaUOgPAuwF8S2v9TADf6v2O3rKLAJwJ4HwAVyolM6HjKgAXA3hm79/5NY6bkLEh1NNBECcXaJcwqSquDPivG0aVUXBy2/RdIiQ/htva9Ync0AGpC0wsW/x6c1mou3KlNbk53dLUmtyijafMmtxYXNnn5KbElfPW5MZOLmWd3LLdlc2bB1U7uXniyq7PRNzzmJMb6pgN1FeTG6sHZk3u2FKbyNVab9Na/6j38z4A9wA4BcCFAD7Xe9nnALy89/OFAL6stT6itf4pgM0Anq+UWgvgaK31bVprDeDzxjqEkADmcd0lOjqd7Hy2apX/NcOiCidXztnjFllua3dlGQ9FLhl5zJrcgbiw/B6KC4ecXEPEpnRX9jq5FcSVi9bkhoRmrCa3TFw5RWDXHVcuUpPb6fTfd1GRG/q7uH5OJU/jKV+dtO9vnuLkyvuyTxp54squ16SkEuz95IFx5dbTSE2uUuo0AGcD+CGAk7TW24BMCAM4sfeyUwA8Yqy2pffcKb2f7edd+7lYKbVBKbVh586dVb4FQkaSmJMrYmnlyuyxTcIk5uTu2wd87GPhFJPvmmDUaes8uXRyyfhQZ1zZcIkBxwW8OL2xmtwa4sqpNbkpTq7rNSlTCIXGVsc8ua592ZjLY06uT8SGTkgxkWuKwbobT+VpABVzz1O+T6FGY779usZm75+Np5Y8tYtcpdRqAF8FcKnW+snQSx3P6cDzg09q/Smt9Tla63PWrFmTf7CEjBmhXhVA/7i8fLn/NcMi1njqT/8UuOQS4Otf968/riLXvAZpU72xWZM7bn9zstQwGk8V6a6MLqCm3K8z58k1t7ewPBZXzlGTW7WTa4oa++Cjdb64cmjf5u/mcjOu7HLu8sSVm+6uHDohxRpPmSe0tjWeSo0r+75Pse8DkObk+kRyXTW5dHJbT1TkKqW+lfKcZ91pZAL3i1rrr/We3t6LIKP3uKP3/BYApxqrrwOwtff8OsfzhIwFWgMf/ziwf3/12w41LjSXt1Hkmk6uK6584ED2uGvX4Lpy/eO7jhtltM7+NnId2ab3ZndXbpMAJyQXptvqE5mx7sYhJxcTwMSk8btr3x6RK+5ske7KeWpyQ3Fln0sLLI4ru4RwzMlNiSub+7OXD6u7smzDN8evCPSicWU5Udft5Batye12/ZHhUF1srNO3a5nr+WE6uW2rHSIAAiJXKTWjlHoKgBOUUsf1uiI/pRc9Pjm24V4H5M8AuEdr/WFj0dcB/G7v598FcJ3x/EVKqeVKqdORNZi6vRdp3qeUOre3zTca6xAy8tx3H/BHfwRcf331247FldsscmNOrtQRi9h1rTuOTq58jjMz2WObzq2mkwuM19+dLDUS4spFa3KlqZXXye30lnniyguNsGp0cmONp1wCVtatawohM64MhJ3cukTu9LTfyQ2JHulgHHM8i9TkVu3k5nGKdS/xEPtMQ3Hlup3cWGfnMiLXN3bSCqYCy94M4FJkgnYj+rHhJwH8dcK2XwDgdwDcoZTa1HvuTwF8EMC1SqnfB/AwgFcDgNb6LqXUtQDuRtaZ+W1aLxz53wLgbwCsAHBD7x8hY4FET+voAJzSeApov8h13bhfqiJXviczM9n0T216b/bfXa7rCBk5zMZTPpFrd11e9Jquv7uy7dS6GltVFVeuqybXJ2CB4lMI5Ykrm7+7xpZyYMzjxMnJcdWquJPrGps4uTGRG5pCKKVBU1GRKwLcHIs9Ntf2TSdXXmt/JiF33ewQWcShjrnMQL3z5EoDijadiMkC3ssPrfVHAXxUKfV2rfXH825Ya/09uOtpAeDXPOt8AMAHHM9vAHBW3jEQMgrIsbEOgTnKTm6s8ZSIXHOeX8HuX9Imt7MstpPbpnOr/J3H8eYCWWpod1xZa/TFZcTJVbF5cifdy2Pz5CbFleU1HsFStibXFVeWbZV1ckNjM+PKoSZIdTm5q1YVc3IlElzEyZW7u3U5uaYAd20/tSbXN7bY3MYpjadCTu70dPZ388Xn65wndxxrosaI6D12rfXHlVL/AcBp5uu11p+vcVyELBmaErkpTu4wjtNf+1p23XDeeYufH+e4stSrFnE55XOUz6xNAt7l5BIykugusvv0Vlx50c+h/3yBuHLKPLhJUwhVEFfOGy8NTSEkr49NIeQTPWXjyrLtOmtyV69Od3Lt9UNObqjxVEqtsm+/KeRxckM1ub7l8r6lZlepxcvLOLmy/txc805uSKCTVhC9xFJKfQHA0wFsAmAeWSlyCamAOkVuauOpYYqSV74ye7SvV2KNp2Tao1EUuf/1vwK33QZ85zv5123ze7Nrcilyyeji6a7s+3lg9YDIXZgiKLHxVJkphMrU5OZ1clNrcn2R4pSx+e56mmOrs7vyypXxKYRc20uNKxc5UZcVuabL7Bu7b/vm39y13HVjoojIDTm5K1a49x1zcquIK9PJbTUpPsI5AM7Qmn0yCamDYcaV7Xhpm0RJzMkVRlHkPvQQ8OCDxdZtcxQ7Fn+/917g0ksz915uUhDSTno1uRN2XNnz88DqHUPkOkRqqPGUPU9u0e7OC9syiEWCzQOvr/GUiFRXB2EgXpMrjYpicWWfoBpGXFmWz8z4T5QhMShCskh35VjkqqrGU1XV5Nrr2idx+Vl+j3Xbdi0zXxNzuJtwctt2kUEApM2TeyeAp9Y9EEKWKk05uW1sPOVrJAn0x+JrPCVjH0WR2+2G33uINr+3mOHwx38M/NM/Ad/+drPjIiQ3WveFqNe9jdTEemtyu5mLW3QKoYXfSzi5sWl6XCJUDsBlndxYXDkkwFO6KxeJK8deLwezkMgNbS8WVw6JXNvhbtrJDdXk2t2Vi7jvRZ3cWPQ+lkooW5NLJ7f1pDi5JwC4Wyl1O4CFjIbW+mW1jYqQJUTbG0899hjw1Jpuc+3Y4V9mloa5zo/yc0jkttHtBLLxlRW5bXxvsevnE07IHl1zGxPSKqQmNxhXDv3nC8SVB2puI8u9ceUEJzdvXDnUKMgUuWVqckNx5ZT6zqriykVqclOd3FBc2TVu+VsWqclt0snN+5nJ9yn0mRZtPBX7LjcxT24bT8RkgRSR+966B0HIUkaOvXXcCCzr5H7zm8Bv/Abw9a8DL31p9eN77DH/MjP55rqmGWUnt4zIlc+oje8t5uQef3z2uHt3c2MipBjmFELGf9YqanK1xJFlqhWXiB3SFEIiJGNObZkphEJOrimI2hRXLuvkmm5pSEQWiSu3oSa3zI2J0Lq+/ZqvL+rkMq489qSI3IcBbNNaHwYApdQKACfVOipClhBtdnLvuCN7/Pa3mxe55hyrrrhyipPbRiEIVOPkyntr0w1kGYvv+0SRS0YHaTw1gUXz4aY6uboLqKnBdeT3RXFkj5NbR1w5xRWMiVilik0hJAfwUE2uKYjqjiuHHEobs619kZrcUFzZ/N01T27s7mHba3Jjjammp91/l5iTG/su1+3khuqBSStIqcn9Oyy+FdjpPUcIqYA2i9yjj84en3yy+rEBwPbt2eOaNe6xmak3wH3jPTRPblvPP1WK3Da9t9i12DHHZI+MK5PWozUW3NaicWWZ69Y7D66nJleWDyOubDpvvjhy0biyHXf2uXpVxJXrcHInJrK/W9m4cl4nN6WO2vVzKnmmECoiclM+M1+M27dfc19tcHLbdLeZLJAicqe01gu3lno/L6tvSIQsLeoUuWXjyiJyn3ii+rEBfSdXajVN5OYyML5ObpGe9aNQk+vrjyLQySXtp9ufQsgUmamNp7ri1k4mOLV5ndwcceW6anKLNJ4ynWBX3WyVceU6anKnprJ/RRtPlY0rFxW5c3PAW94CPPqoe9yxuLL5fmM1uUVuXPg+M/P3IjW5psh1fb7yXBmRK3Gztl1kEABpInenUmqhyZRS6kIAvA9PSEW0ycm1j9MzM9ljXU6uiFw5R9ljk/NH2ZrcNglBoFwddpsFvFyj+s778jlQ5JLWo6UmN9BdOVSTi64hch1xY1kmv9vLk6YQShG5HuGQ4uTGanJDTq88+pzcNsSV8zigphAsOoWQz8k1t1dknlzzZOl6H5s3A5/4BHDTTYPLut3s7xj7u/vel9lIzLduKK5siuCqnVz5fkxP1+PkymeqVLtOxGSBFJH7nwH8qVLqYaXUwwDeBeDieodFyNKhzU6uLC/q5H7mM8B/+2/+5SJyfTdZQ3HlkJMbu8E7bGQ8RSLLbXdyJyf711u+7xPjyqT9mI2nPBHlaE1uL3LsqslFoLuyCGT4RG63/zrv/jvu19gHR5cYizm1RWtybSe3zriy1vGoTFEn1/fa0PZS4spKFXdyQyJX6nwPHfJve2oq7LCb+7L3XVdH7LI1uXZc2XdTpkikStYPTQtFhk608ZTW+gEA5yqlVgNQWut99Q+LkKVDG5zcWD+LoiL3D/4ge3z/+93LpSbXdX6INZ4y3dDZ2f57MJe10e0EFovcFSuKrdvG92Y7ub7pHOnkkvaj3U5srsZTvrjyPDAxlT5PrrcmN3BxLs2yqqzJLTuFkL1+LK7si7aG4sr2nHOyLRdVx5VDYnB+PjvYh0TuihXFIledTvZ5Hj7sFrlHerN/uhpYmBPSy2PVNbkxJ1fW94lU1w0Vc3nMyZUTkrjO9vpl4sp5kgOkcVKcXACA1no/BS4h1TNMkWv3s/CJ3LrjykWcXHOdfdaRqc1CEKjGya3rvXW7xbdpGzG+7xNFLmk9utvvrlw0ruxqXAUA3TlATcPr5La1JteOK8dqcm1xYoqWlLiyz2VOiSu71rcx33vdNbkpTu7MTPhE7Wt2UJWTC8Tj1LGa3JCDHVvf932amgo7uSk1ub6xma/Li/l9bNtFBgGQQ+QSQuqhzXFl+b2syPWJOXGIfU5uSuMpYGmK3Lriyj/zM8CxxxZbV65nYjW5c3N9c4GQduKLK+d0cidcIncWmFxWYp7crvv5gW04th07OJpCMyRiQwfllCmE6oorm/WhsYNjnU6u6+8aazy1YkUmSG1Bl/KZhU4IcrB1idy6nVwzjhxaPxRX9nVHTq3J9d0AqMLJZVy51VDkEjJk2hBXjtXkuupe8/D44+7nZX8+Jzel8RQQF7ltSxKV+czrFvCbNwP79xdbV65XYnFlANi5s9g+CGkEbYpcT1w5VhPriyuLk+vrrmzX5PriynU4uaboCNXkutzUWE1uE3FlU1DFDo55Ra7cwSvaXTnWeEo6PfpEpu9EnerkhuLKISe3bFw5xZ0P7bcuJ7esyA2NnbQCb02uUuq3Qitqrb9W/XAIWXq02cmt6ri9axdw0kmDz4dErunkhhpPAYNOc+zcN2zqdHIffhh4xSuAb3wDeOpTi4+xCLaTG/o+7dwJrFvX3NgIyYfuC82iTq4rrtztZNuemA53V5Y5dp3La44rS8dYnzNmC037AG0euF1Or8/JtaOteePKWoddQ5uQKLUpW5ObEleWBg1zc/2DqLk81GCpqJMr20pxcl2OqrjnsSmEisxtbH6fUqYQCsWdQ8tZkzu2hBpPvTSwTAOgyCWkAtrs5NozG7im+knBV4OZ6uSGGk8Bg85j2+PKZmw3L7H39slPAj/6EXD11cB73lN8jEWwb+qHnNy66rwJqQQRmnbcOKUmV2tkInnSEXfu/aefWFZ+ntyUuLL9mhT3K2WKIJfQTI0rF51CSARTzMlNrcktGlfudheLe9c2isSVxcm1OxLazTN827bfk1DWyZXXTE8PLrNvLBR150ONp1Ljyq6xme8t5FDbTalSMAV82y4yCICAyNVa/16TAyFkqdKUk1smrgwAe/YAJ564ePnHPgZcckl2o9jsbiysWJHdPPZNGRMTualOrm/cbRW5ZZxcea++KPbatdnj1q3FxiYUOefncXLb9pkQspjef4BC3ZWl9tTRnbnbExwTZuMpV83thH+5xJeDU590rUf5NUEwxWpyZQoh8zlzW2WmEKqqvtO1vk0ekStC0hRMtshNiSuHpugxRa5rua9DZCyunOLkmiLXdfNB9l/kM4s5ubF5cn1ObqwuKdXJNceRB8aVW090CiEAUEq9BMCZAGbkOa31X9Q1KELGkfvuA1avBk45ZfHzTTm5KXFl3w1cIKurtUXuf//v2ePu3X1xZXLssdl5tYiT65pCyHdN4rtmGEeRG3tvEgvftq3Y2IQDB7Lva96xmTW5vhvzrmWEtIteTe5AXNm8UPaIXHmNK+7cFSd32j+FkNTkLtxlstzQhdfXNIVQandlGY9ruTzmrcktE1eOiWCbPHfdTCdXfrejTalxZZ+INePKruWhGxOhBhQpTm5KXHl6ulhNblONp/I6uWVFrinQeUJrJdHGU0qpTwB4LYC3IzvivxrA02oeFyFjx7Oe5a5BbHNc2Txuu5pHyTn58GH3/qVLr8vJ1bq//VQn19ddOebktq1cps6aXPlblXVyi8SJ5Zyf0niK1wSk1Whznty8cWVD5A7U5Bpx5dgUQi4n1/fzwBg6ix8X9h9xt+zIsHnQtUWqvb6rJtfn5I5yXFl+z7M9M67scnmBvpMrotTelu9udNVObuiucRGRm1KTG4srl6nJjS13LUuBNbmtJ6W78n/QWr8RwB6t9fsA/AKAU+sdFiFLh6biymUbT+3ZM7i+iFxf92URmS4nN3YTPdZ4KsXJHefGU6EeJEB5J7eIyLWdXMaVyciiuyg8hdDCayYGa3rNuLJ3CqFuoCbX/LlA46mUA4hPSJpObWy5PIac3FB01Te2PE5unrhyTKSkiNyQkxvqriy/r1yZPfpErm+e3E4n3HiqrJMbqslNjSsXmSdXXusTkbE7vvYNnVBNbhGRarrUPKG1khSRK7d+DiqlTgYwB+D0+oZEyNKiDU6ur9TH/D0kcn1Tzsj2XU5uaH55eS7UeCp20xwY77iy77wuy4fp5KY0nmrbZ0LIYmQKoYkCNbkJTm5oCiF7nlzk3b/5upxx5VAk2K6ptZfnrcn17buK6KtruU2dTm4orhwTufYk4rE66mE7uWZ35briyik1uWXjynlhXLn1pNTk/oNS6lgA/wvAj5Ad+T9d66gIGTNCx8+mnFyXoLLPnSHnzbW+iFx7nlpBtudycs0byK73XoWTO84iN+bklk1PktHSwwAAIABJREFUVeHkhm6ct+0zIWQxZuOpvE6uiDlHd2UzruybQmihJtcVVzb/49Q0T64vXprq5PriyiI6YnFlV9xY6+zfMOPKoVoMexsuNzTWXdknclPiyqHYUh4nN9QYy1WTK92VyzSe8jm55vepCie36riyeVOGJ7RWEnVytdZ/qbXeq7X+KrJa3Gdprd9T/9AIaZ6HHoo0rCyIL84LNOfklo0ru47hVTi5y5enO7m+c9JSFLmha1TBd/MhhFyL1F2TW8f3nZDc/PjPgc1XDz6vNeCa57Z0Ta4ZVw7V5E6if4lm1sWWFLmxA0jIeXPV5JZpPOWLK7vixrF9y/p5ndzUk0SdTq5sa9Wq7NEnckN3o4s6uSlTCKXU5Pr+5jEnNxT5zVuT63NyQwLc9XMq5ntnTW4rSWk89Ub5h6wB1YW9nwkZKx58EDj9dOA736l+2yGxMUwn10wqTUyE48qu8UmfjDJObkjkhhpPmesspSmE7PfmiysDwKOP5t++dFQu6uSy8RQZKR7+O2DrNxwLejFMNQFvXNjXXXnheRG55vpGd2Wfkxuqyc3deMrjXsVqcss4uabIFQfWXF4krmxGoauaQsisZU15rT2FkOs15lhc64dEpNw19onc0J3NojW5eeLKRWpybXc+tH5I5BZ1cmPfF9fPqTCu3HpS4srPM36eAfBryGLLn69lRIQMid27s3Pxzp3Vb3tYIjfVyRVhUpeT6+rMbIpcGas57eD8fH+Z73prejoTijEnt203WZuIKwPAI49kXb3zsHp1JnCfeCL/2OzrHTaeIq2nOwd0HQdHLTW5ZeLKvQ7J3rhywMmFT+TmdHKL1uSGRGyemlx5jVJxJzcUVzajq1XGlVNFrhk3lt9d2zPfi71+2ZpcWb8OJzel8VTReXJjQjM2T27RmtyUemF7X3lgXLn1REWu1vrt5u9KqWMAfKG2EREyJOQ4XofYbIPIDTm5PpEbaw6VWpPr2rctcuV8Ye4v1Hiq08nObSGRO87dlWONpwBg797825fE3LCc3N27gX/5F+D88/Pvn5Dc6Pns3+ACuEWuT3Bi8PloXDlSk7sQtvPsM1RbU1VNri+OnDqFkDxvilpfTW4oruzadxVx5ZADapLaXVlOpHniyqk1uSHHMyRyxck9dKh/w8HedoqT63JUZd9lpxAK1QJPToZFru8ztOuF63Ry23YnnQBI665scxDAM6seCCHDRgRHHYKojMj9138Fzj03XNcb2/by5WkiN3Sj0/V3kRuoMSfX11hKxubafqzxVOgm/FKKK4ec3CLvW/7mRWtyQ42nUsb2whcCF1wwOIsGIbXgc3JhzpPrE5kJTu7AFEJGXDk2T24sruyNS8MvcmMHkFANpenEpsaVzedTnNwm48qmOEx1clNErmtsKXHl2BRCoRN1SKybotme1D7FyTWFqutvHnJLTSHoGl+ZuHLKDRszdRA6IZWtyW3bRQYBkFaT+/dKqa/3/v0DgPsAXFf/0AhpljqdXBEME47/cTGR+853Aj/8IfD97+ffrxy3ly+vJ64sz8WcXNe6MZHrcnJtkRS6TgPaL3KLfNfyOLlF3resU4eTmzK2O+5wr0tILeg5t5Oru0ZNLvpi0XRpfSIzb3flPPPk5nVy88aVU2ty88SVZb2Umlxf/WZb4sqxxlOu7WkddnJTG0/54sqpTi4wGFlOdXJ9Y491V7ZvmoTizD6nta1ObtG48qFDwMknA9dfn3+fJBcpNbl/Zfw8D+AhrfWWmsZDyNBoIq4s8V6TmOCR817IyZ2dzToYn3yye9spTq7r3Dk/nwnF2dmwG1uHkyvnVcB/PRWb+qitIlfOp0WcXLM8CgjfnC7yXZb1q3ByXddiUkcdGxtFLmmErkfkmnFlIBONyqivnVgWaPwkz/ec4K7xH93VXblrH6C6qKwm13aJTXfM/F2QA2usJresk5sSV84jsOU1pnOXInKr7K7sc1Ttmtq8ceXY+rHYtbm9gweBpzxl8fuSbcujS4RLd8q8NbmpcWXf+xKX2BXtSYk1hQS26yZKHkyXOs9Fxp49wLZtwObN+fdJcpESV94A4Lta6+8A2AngOUqp6XqHRUjzNBFXlnOYSarIdTVGFK6+GjjjDL/gKePk+kSo+VyKk2tfj1QRV051cttWLlNlXLlqJ7eMyLWvZ1zfp9RryrbdmCBjSnfeHVfWtsi1ndzpglMIzfXX9zq5ncSa3BJx5SI1ualTCPkO3LaTO+y4silKU0RubJ5cn9g03dKyNbm+uHJRJzd1CqFQXDnmlqZ8Zr6bHhKNL+Lk2lFq1/LUmmwX5vc1z/p1XmySRaSI3FsBzCilTgHwLQC/B+Bv6hwUIcOgrJM7Pz9Y7iKkOLm+412Kk7t9e9YN1xZNpsgt0ngqJkpSnVxzLIKMJ09c2efk+q4ZyjSe2rKlvrrQKkVum5xcOefLdUXIcEi5piSkdnxx5YWaXKtudsHJnU6ryQ3GlUM1uZMJNbkeJzc0zZD8By9Sk2u6qSlOrv0aOYDXFVeWeGqRxlNVO7nm9ky3tIzIDcWV8zi5rm2nxpXz3liwP1PfjQ3X98EUuUVqcu3vg2v7oZsDMYrW5FLkNkaKyFVa64MAfgvAx7XWrwBwRr3DIqR5yh533vte4D/+R/cyEbki6EyqiCvL2H2xXZ/INa9JfN2VU0RuzMl1rd/2xlOnngr86q/mXy+FKkRuXTW5ZePK8pm5vk+xa0rzO87zP2kE7xRCXQzElc3HoMiVL6+rcVXZeXJNJ9cncgNurx1Xjjm5VdbkpsaVXYJIDpYSm7X3La/PW5ObKnLkrmsZJ1f+rmVFbh1ObmrjKZ8QjTWeitXk+u6KynetLie3rMgtElemyG2MJJGrlPoFAK8HIDOmp9TyEjJSlHVyt2zJ/rkQEeg6jlYhcn1jryKu7Lt5bK4fcnLl/OObnmhmZvG2zHXLNp4q6uTK+fSf/7l6R1Hr/vbb7OQWmSdXbsoDxZIBjz7a/5lOLqkdrTNB6HVyA3HliWVpceWBeXIdNbkD2+lm68Xiyt7GV4HXmELVJ2hiTq6v7rWquLJLEElMasWKarsr+8SbTZnuymYkONR4KkXkuuLKMbF25Ej/ZFjEyZ2dzQ7cRborh75P8rvPJTajQaGLJ99JJebkxm4OxDDjyhS5rSRF5F4C4HIA/0drfZdS6t8BuDm2klLqs0qpHUqpO43n3quUelQptan378XGssuVUpuVUvcppc4znn+uUuqO3rKPKWVO8EVIdYQ6AafQ7fovzEXkhoSib93YND1AcSc3JnLlvO47hoecXK2zv0nIqZWx+ZaHGk+Z53Xf+56ays6PeT9T8/U33phv3TzbrsPJdRkIeZD1i0S1TSc3lqqLiVye/0ntiLgNNZ6yp/kRATsxDRx6FLjxBcCh7da6IuYm4Z9CaJmx7QadXNNt9blnvkhw0SmEZD27pjdPXFkcyJmZeHflPHHl1Lhpme7KKU4skJ3sJyf9jafkMzP3LSfaUFx5dhY49tjs5yJO7uxsdqJ2idxYd+VQMkB+D8WVZV3Xd92OKxepFy5TkxsS6CEochsjKnK11rdqrV+mtf4fvd//TWv9Rwnb/hsA5zue/4jWen3v3/UAoJQ6A8BFAM7srXOlUgsdGa4CcDGyuXmf6dkmIaXxCcVUul3/MUuin67lMZErz4fio76xV+nkhsbuEuD2TVafk5unu7It4GLXDEXOP/ZYr3NMmLZrF3D22cWaI1Ytcn3vHSg3RVHRdUNxZdPJdW2fTi5pFBGcvsZTUlMLuOPKT94L7Po+8OTd1rqGk+utyZ0e3La5ftI8uSVqckMHz1hjKdPJdS1PnUIoT1zZ5eRWEVdOdeLKdFeOnZDM5cuX53Ny5W8Qq8kVkVvEyT1ypC/AY0LS931yfSYSa0ppPBVycsvMk8ua3LEmKnKVUmuUUv9LKXW9Uurb8i+2ntb6VgCPJ47jQgBf1lof0Vr/FMBmAM9XSq0FcLTW+jattQbweQAvT9wmIbkoG1fudIo5ubH9yvFw717/vss4uTIbRKhpYxEnN8WpDS13xZXt66mYyC2SJLLH4oqJb94MbNoEbNzoXv/JJ4FvfMO9rAqRG7tx7tpXKrJ+0aizGVd2jS01rszzP6kdEZy+eXKhMid24Xcs7q68sB1r/aDIdcSVXfPkmnFlr5NbQ1w55LyJC7hihb+GRLZtPqbW5JrxVHN75r5nZqqLK+eJm6Z0V/aJ3Fhc2RSxy5blq8mNuZlAtU6u6/sSE7m+mtwUZ79MTW7KPLlFRa4t0ClyW0lKXPmLAO4FcDqA9wF4EMC/lNjnHyqlftyLMx/Xe+4UAI8Yr9nSe+6U3s/284RUThNx5TJObqhGMsXJ9YnckPMm5/WYk3vggP/mtIjYvE5urPFU6PrEvCbIe5PVHqvr7ybXID53/ZprgN/8zazrtU0VIjeU/nKl5PJQRuTGvk/mNUVM5NLJJbUTcnK9cWXDybW303+i9xhycpf5G0/BcnK9Nbk1xJVtJ9c8wEhkZ/XqsJNbpibXJ7ZiAlt+zxtXTm0cdORIdrIKHcDKxpWnpuJOrh1Xtp3YvE5uyhRCsZrcmJBMaWTm2u/cXH/O5pDIDTWoCDm55p30vCI3logIQZHbGCki93it9WcAzGmtv6O1/k8Azi24v6sAPB3AegDbAHyo97yrzlYHnneilLpYKbVBKbVh586dBYdIlip1xpXL1OSmOLllGk/F4qW+KLO9P9vxtEWuz8mVxlOufYcaT6U4uUXOP/ZYXO9brkF8Nx7k896xY3BZVSI3xckt03iqCifX933y3XgwTQaKXFI7sZrcRY2nAiLXXn/B7Q2IXBVoPCXz5C5EWDzx4xQnNxRXjtVBmq8H+gf5Vavy1eS6nNxQTa6Mz1wucWXTya0yrhx6rdzBXrasXFw51nhK4sp2QwTzM7MjMmWdXPNkKY++uHJI5IaEZKyRme8zmJ3N3pdrv+b6ZZzcojW59s2iPOvL50uRWzspIlcuw7YppV6ilDobwLoiO9Nab9dad7TWXQBXA3h+b9EWAKcaL10HYGvv+XWO533b/5TW+hyt9Tlr1qwpMkSyhGmrkytCqIiTmxJXDjUKSo0rA4N1uWXjyrHGU3Lu9IkpeU9l48quz0XOUb7PRETw7t3hbVchcn2JP3tfqcj2iqwb+z7FjJPQlFOEVE4wrqyx4MRmL+49nyJy7biyeWE/i4UYdHAKocn+Nnxx5RQnNxZXjkWGY05uHpFrOrk+USOunC1sTCc3Na5cVU2uHPBNJzfPFEL2PLn2cvOEFXJyXY2nzBsP9naFI0eAo4/OfvbFlWNO7vLl/rrZUHflUJ21+b5cNz3m5rK/Z8zJDdXkhgR4mbiy+V1nTW5rSRG571dKHQPgHQDeCeDTAC4rsrNeja3wCgDSefnrAC5SSi1XSp2OrMHU7VrrbQD2KaXO7XVVfiMARxsYQspTRU1up+M+Fqc4udIk0TeuIjW5ZZ3c1LgyMChyy8aVTSfXdz0VcnKl1rhs46mQk+uLK8vyXbsGl1UdVw5dUwzbyfXd1E8RuXRySe3oUFy5d/Fuu63mFEILL7X+Iy+I3MmeSDWd1bm+QPY6uV0jqjyBUnHlrifqkVqTa57QYnFlU7SYjz4n1z4wSyQYCDu5qXFl13szD8qpIlcO6CL0gHxTCNl3Xe2xpYpc1wlPamKe+tTB/Qqzs8BRR/V/dm07pfFU0Zpc32eSElcWket6X7bIjQnwKmtyGVceCaLz3Wqt/6H34xMAXpi6YaXUlwD8CoATlFJbAPw5gF9RSq1HdmR+EMCbe/u4Syl1LYC7AcwDeJvWC0fptyDr1LwCwA29f4RUTtnjjnmzWo6pgpyzYkJRElGucZVxcmdm0mpyXY0XU503+9xZReMpGZur8VRIMJmfQR1x5VQnNyZyy9S9xpzcIrXI5vi63cWiNc/YAH9NbkiA08kljRJ1co24sojFPI2nfDW5IpBDNbniQYSc3CKNp2xXME+8NDWunFqT6xJTPpHrcnLzxpU/+UngrW8F7roLOOOMxaK408k6CT70EPBbvzU4LiCfk+sSsRKLkufs16aIXPvuoYjck07yx3qPHMkuAqanB7ed2njKF1dOmYs2VpMry+1ti8hNbTxl7zs0NtleUZEb+38UgiK3MaIiVyn1OQCXaK339n4/DsCHerW5XrTWr3M8/ZnA6z8A4AOO5zcAOCs2TkLKUtbJNR0wW+SGIskxkWt2MDZFhEnRxlOxGkpz1oTY2H03iIs4uVovfq9FnNwyIjcmRGM1uWI6uOLK5nso4uTKZxJzcuW1Nk88Afzbv2VTILmwDYg8Ijf2fZLlLpfXHi+dXIJuB9jyf4BTX9kXVZVuf37x4yKsmtyFuLK4T2Zc2dN4ytddeWHdBCfXFrnmHLxFGk/FHKhQTe7+/ZmgEMED5JtCSF7ri9gcPtxv0mAvN6cQKtp46oEHssdPfhL46EcH75Sec87gewLSRa4pXGNxZddJxtd4yv7MzH3HRK7WfZG6bFkxJzc0T678zX03LkKfSczJlXH7nNxYTW7IybXfdxkn13dzwQdFbmOkXL48WwQuAGit9wDwXB4RMjy++13gvvuKr19FXNm3vlnnaJ8/Y4LKFEK+eKxv7KbQLNN4KuTkyjWJLdjKOLny9wo1ngo5ubZArsvJHWZcOebk+oTkb/828JznDJZmCXJdAOT/v5DyfUqNK/P8T7DjFuB7rwb2/Kie7euQk9tzU71x5aJTCJlxZUtAA4YQjMSVJ6bd7hawOKLsE7ly8Z/Hyd2/P4sqy/r28jJxZWlqIScE+8Adm0LInNLFdwdQIr3XXNOPqsjBNCRSiji5dcSVXQfPmMiVk8yyZe5ty/Ky8+T61g2dsGyR26STGxPIMWICPQRFbmOkiNwJY6ofKKWeggQHmJCm+YM/AN7//uLrVxVXjkWSXaJEiEU4fXW5MSd32bLsNS6BHRMlocZTnU52Y90cg7kMKObk2gmq0Dy5IXEu26ha5Mac3CpE7rXXAl/6knv9MjW599yTPd5/v3u/3W54LtsQ9t+9TOMpOrkE8736iXnPHZmyLMSVHXcfg92VTYcXg05urLvyQlxZxJpLlHqcXHmtmioeVzYbFvgOIL7GUyJyizi5obiyKSSBcE2ua9+2S2yPHegfcHftAu68c3G8WbbvWk/Glqe7ckpc2V4+MVEsrjw9nXVPdolcs2mWa9v792fvS8Ydc3J9Tq1vXTsZ4LoYSmk8VbQm1xdft++kM648lqSI3A8B+L5S6i+VUn8B4PsA/me9wyIkPwcPDjY/ykOVcWXXslDJij0GE1MI+URVSOSKq2eO0dy3KXJdbmtMlIjIrbIm104SAYPn71Qnt0jjqbJx5Sq6K195JfDXf+1e35cmlOUhJ/cZz8geRezamCI37/8FO67sM4lSGpnx/E/6wrKmOx5mwyg7MgyrJlcEpe70GkoZItfr5E66Ra4KOLlm1FkenXHlKRSOK4fuANpOrikkDxzod/F1HYBsoZrHyTVFrIzNXH7oULZdEej2vm3BZC8HFp+k9u9fLILuvXfx+3StV2V3ZftgJ8/n7a68fTtw4on+uYdNge6KK5s3LmSMPifXF1eWz8O1PJQMiLmhqU7u1FQ2BpfA9s2TW1bk2hFyxpVbSVTkaq0/D+CVALYD2Angt7TWX6h7YITkZXbWH8FMoYophMztCNI12Scc8ji50qXZJtR4amKif951ua0pceVQDWUTTi4wOItAnTW55lhdQjQWV5brtTJObqcTbhYm13q+G+s+J/f007NH85pOkMRfUZEb+z6ZY/N9n+R6hE4uqV3kmtsdmAZIRK4jrixdkwW7u/KCaI3U5C5EVExRarjAsg1X46mJEiI3j/OWJ65sNocCwk6uLQxcAtl2cu3tupzcUFzZPKDOzvb/Fj/7s8CmTf1l9om2bE1uSlw5JnKl7tUVVz7ppP57z+vk7tvX77wsY3Q5ubF5cn3rhr5Psbhyak2u66YJ0I8r11GTa954yFsT5btgI5WT4uRCa3231vr/1Vp/XGt9d92DIqQIs7OLE0d5KXvckWOc6xgP+COgeZxc39hCTu7kZDmRKwkr3zm9TifXFLllnNyiItf3vpuIK8dErozPdc0RujEh53SfyAX635eyTm6R7spi5PAmN1kQa87GUBVgitOBfXT7cWNzLCJyzcun4Dy5tkg14srA4Dy6pgss23DV5BaNK6fUUKbU5LqE5qFDi5tSxZzcPHHlQ4f6B4eYYPI5ueYB9ciR/t/iVa9a/LoqRG6nA1x9NfB7v7e4sZRLgEs3QSD7+7kaT/kiVzGRazq5y5cXc3JD8+SKkHSta4tY8znzbxCLK8ecXJ+bmuLksiZ3rEkSuYSMAm11cm2Rm9fJnZvrnx98oijUeMqMK8dqV11ua2pcuUon1xVXtm/mytjraDwlr5+ZCTeeOnJk8HpEngfKiVzpw+JaP/TeTPEfumniErmyrSqcXNf+Y1Fqs+8Mb3KTZuPKPifXiit3HXHloMh11eQaTasG5tGNxJXLOrnynxDwx0t9NblmXNnn5MoJQbZvvsauyXXFlUMiV7YdiyvHanKB7CAuf4uXvrQvdoDBiE7qPLl2XPnb3wauv746J1e2YceVfSJXa2Dr1v52XQI6xckNzZM7N+dvWmW+71hNbqjxVKwmV7Zvjy3k5FYdV6bIbSUUuWRsqErkVl2TawuHvE6uKSRjTq4tmqpwckPH8LqcXFdc2b6Z20TjKZ/INa8TXJFlWb5vn3/GBqAaJ9eXDvO9b9nmffe5b3wD/s8sRh4n1ydyxayhyCW1O7mmOB3YR6/xlD3Nj+70HdqFde3GU4lxZWDQyTWjzoBD5Bo1ub7uymVqcuU/cREn9+DBxSLXFqOmk+uLK4emELKdXF/Tqzxx5clJ4LjjgFe/Gli3LlsWcnJl+747kKZoevLJ7G+SV+S6Thquz0xrYMcOv8j9wheAF7wg+9nXXTnm5MrdVl9c+eDB/o0Pn5Mbiyv7RGoVTm6su3JVjadYk9tKKHLJWCDH4VGIKxdxcn1uqfka1/pyjE9xckPz5LbByW2y8ZQ5tpjIdUWWze+h3Xyq7riy6ZaGnNxDh4DHHlu8LPZdjWF/n3zXzykil+d/MlQnVxpPTVhxZXQdNbm2kyuRYkPkfvOXgEOPYVF3ZQBZzW3AyfVNIaSmFj/v2r/9M5AWVzZFiSkuUmpyizq5sZpcc9tF48qmeDTjygDwt38LXHdd9rNP5MqB0RdFMZ3cTifbzqFDi+PKvthuzMl1naj37s3ek0/kmnMqipNrC+iYkysnDNc8ud1udrJbudK9rh1Htt93LPJrOrkukWt/n1wnnKbmyaWT20oocslYIMftNsaVm3RyfSK2isZTsZrcovPkuv4uw248lRpXBtwi98iR/jWgHVk2xX9Zkes7r8fcUnOc9rpAdd2VfXHl0NgYVyYLNFmT64ocL6rJFTeyMyhy7SmEFjogG7Hmnd8D9vxfR1zZV5ObEFeOObmuul07+prHyXV1V7ZrckXwmK+xndyiUwjJHbCq4srmAUupvtgLxZWB8AnRjCs/+WT2nNz1LBpX9rnv5hy58t7N93z00f2ffU7uvn1hJ9cU+PZdVbng8olcO9JrPmf+XLTxVOyOL+fJXfJQ5JKxoAqRW9bJrbMmt6yT6xO5KaIk1cn1xXJDTm5s2kA7rlzUyS0aV16xonhcWa6X7HSBKaBjNbl1Orn2z7JPoHjjqdhNkzxxZZ7/SaNO7oAb23Ny7bhy5xAwOZM4hdDE4tfN78/iyiqhJtc7T27vQDs5g2hN7sQ0BtzeWFzZdnJNgeqKK+dxckNx5ZQphELdlVPjynJCskUu0BeFtpNrdigG3Ac3GYvUM3e7/e3Io++EZzaekrufrpOdbCNV5JpzKsoUTK64su3kSpt9+73bNyYO9uaxFpHrOknL87Ga3KJx5dgdX5+TW3VcmSK3lVDkkrFAjsNl4spVObk+pzbk5IbcqxQnt4rGU764cqooKeLkmud8c99mukuwb+aGBJOMGyjWeCoWV05xcuW6IST+Y05ukcZTqTW59s+yLlCNkxtqPOVaJvujk0sWaHQKIfs/i914qrd89glg2bFYdPnkq8kdELkHBuPKdvdke55cO67c6YmUiZlAd2URD9P5phAy7y7abumRI9nyInFlEShl4squKYTyxpXn5vrjt+PKQP+gHarJBfx3hM39dzr9O6CyPZ+Ta743U4Sb2zZvTMi+U0SuUsCf/Rlw/vn+uLLt5JrjM51ce/u2yE1pPBVzck0xm9J4qqyTW+c8uVdcAXz0o4PPU+Q2BkUuGQtMJ9eX4IpRtvGUHK+KOLmhC3vTyc0bVxbBkxpX9t3UDzWekvNbUSdXzjEpTq752YYcyyNHFhsCRUVuqPGUpPZ8IleuG8qIXNdyU8DX5eT6bsjEiH2f5PsY6q5MJ5csMMy4sjSesuPKc08A08dYcWV7XcONNUVu50BvCiE7rmw6uRI1rsjJzSNyDxzIHletGhSo4grGphBKdXJFMMlzeaYQKhNXNg/MpkMKZGOfnCwWV3aJHhG38rfziVyzYZfsw3RcfdGkFJG7di3wvvdlfzvbyZ2dzf7ZTq45PjmBFRG5sRsPruUuketzcs3vcoqT67rIKCtyfS40AHzlK8Df/d3g8xS5jUGRS8YCOQ77Ip4plD3ulKnJTXVy88aVyzq5ElcOiZLUxlMxJzd0/gHcPS98AtwWuUWTSKGa3OOPz342E2HC4cN+J1fG4tu2OQZfXFn+LmVrcn2fWRknNxZXZk0uSaZ2JzcUV+4iE6lWXHl2b0/kmnHlkJNrXGbNH8Bgd2W75tao53Ut7/ZEymTIyRWh7Igrm8IuReTKActcBpRvPCUHChEvsSmEXE5uLK7sqvGU8ZvdlQWpy63CyT18eHGbfVnPdcIza5ljIteOK08cNiAKAAAgAElEQVRM9E9GLpFrurR252b7xgXgF7nm9Enyd8/j5KbU5JrryL7L1OSG6surjiubEW/B1w2VIrcxKHLJWGAet4tGlss6ub648ig5ufPzi4/TZlzZd+M6tfFUnppcX+Op1Jrcw4f910opmGN3Cc0jR/rlW755cmW5z8mVmtw77siuUWQ6Q6GKmtyiceWiQtMuHSvSeKouJ3d+HrjqquI3wcgQaJWT2xvLXC+uHHJy7e7Kwtx+R1x5Mtx4CpbI7Rgit6iT66vJFSG7evWgMEhxcu0phGJOrrnMnkLIFmymk5vqCrpqPEXkuuLKQFjkykncV4shyyYmss7HQiyu7HJyX/ta4Pbb++/NVQeyfTuwZs1iN9P8m9hRZHueXPlMQ06uHVc2l6U6uWZc2SWCfe572Zpce55c19jKNp7yfaZA9jm5GsVQ5DYGRS4ZC0whUbT5VF1x5TxObl63FMiO40UbT9mCSdaxl/vqXgF3CZH5Xso4uaG4ch4nt464sq+xlNaLl/vcUhG5990HPP448NOfDr6uaE1uqO51bq5/vVF1Ta6vdMzcfsxlrsvJ/fSngbe+NSuTIiNC7Y2nzJpcT+OpBZEqceWek7uoJtfXeGpyMK5sd1e2pxByzZNrurESV55Yhkbiyj6RW8UUQsCgmEpxcsvElZcty06IrsZTQHZ30hVXVqrv+oXiyrL/PCLX5eTecgvwm78JPPxwOK4sUWXZt+3kmgLWjivLuFKcXFPk2u5+yhRCsc/M/j5onf2NQzW59g0bl5PbVOMp1zbo5A4dilwyFlQhcpuKK1ft5JrHVV8cOTWubL/GFKIucS7rLVtWj5MbajyV6uQWaTxlx5Xtm8hmGZPt5MrrY42nROTK+nJT3Hxdt+t2amPn9Vjdq++miX1zO4/QlLRWW51cuR7btq3Y+nv3Ajt2VDceksAw48oLTq4RV9Y6azw1UJPriSvbNbnOuPIkFonY2BRC3SOZwLWfX7R/uQBftnjbwOK4hS2KTJFrixI7ruyryU2ZQijk5KbU5BaNK4szKA2YUp3c2dlsXPI38XVplGUTE4ubNYho9p3wTCd3meHyP/44cPXV4bhyTOSmxJVTanKluzLQ30eeuLJS7u6Rstzer/w9Y05uqCa3icZToeQARe7QocglY0EbnFyfyI0188kjcl1OrvlcGSfXJXJTnFyJQ/vEnO99V+Xkuq43zCkVy8aVZV8m4hTb6S/ZNxBvPDUzky0LiVwg/Jm5zuumkMz7fSrj5JrXK0C48ZRvbKbIrdrJle0WPT5ccgnwildUNx6SwLDnyTVFqu4AnYPZ65YdG55CyOyQ3DEucn3dlbvGf5RY46nOEWBieTa22Dy5RePKeRpPla3JledCUwh1u9mBMqW7ssslFqTGUw7cpkgSfHFlORkA7pOOLQZNJ1fujh11lN/JtUXus56V/fvXf/VHZPKKXHnf8pmmOLn2PLlAWOS67rr77sqG3Hc5OZXprhxycs279PayFBhXHgkocslYUGVNblkn1+d4FnVyTect1i23aie30+k3ngrduJ6ertbJdZ177fN3yBUs23jKjCu7xi7XSq5pB+X3WE3uypXZduU6wT4X2ud6c33fNaqsF3Jy5+b61yRVdlc2r1eAcOOpkMtcV1xZ/g8VPT488ACd3MYR8TeMeXJd3ZVne+5cqpOrJoAju/rPz/dqclXAyV0URwZgTyHUnQUme814YnFlNT04NVIorixC1hVXtg/KttOrdXp3ZVOI+pxc8w6eXa9bJq48Pd13NPPElVNFrohBU+Q+9lj26BO5Bw/2D8p79mSPL3kJcPbZmch13ZjQupiTq3V/3yEnV7bjcnLtmlzT3bffV2h5yA21Y9JlanJDTm7RmlzGlUcCilwyFlQZV666JrcKJ9c3F639XNVOrtl4Km9cuUxNriS9jjmm/1yeeXLLNp6S1/tuLsg1z/Llg+cwuR6LxZXtKYiKOLm+zyXm5PpEbuyGTAjbfS8aVw7Vp5dB/g8VFbk7d7qbjJEaqdvJXTRPrkPkwoorz/WEi914yluTOwEc2dl/ft41hZBVkyvCW0SuM668PBtbNK5csCbX1XhKRK4c2GynV/5z5G08ZQpZ0xUzxyYn9bLdlV1xZTMuJO+viJNrN2gy48qPPZZt19cEybw58NrXZvPa/sVfZCJ369Zsffvu4b592cEsJHJdjafMseZ1cvPGle3l9vjyOLm+KYRiTm5d3ZVNJ9eXHOh0ss/IHjtFbmNQ5JKxoMq4cptqcqX3gpxf8jq5MZFrXu/YIlfrsJCMxZXLOLkukWvezJWxhWpyq4wru27aL1+e7cPn5KaKXDENfCI35L4XcXJTanLLxJVdTUDtsYVErswYUbWTK/8HKHJHiNobT4WmELIaT+nOYid3UeMp28mVL7ft5EpNbqC78oKT2/vCDsSVZzORqyZQqPGUGdHN03jKFrm2myoHsDJxZVNImqJF/tOG4sohAS2YIjdvd2WzVjZvXHn//v7JLObkrlqVzWu7ciWwfn323MaNiw/6WvfdYZ/I1drdeErej4xL3rOQp/FUTOS66rhTG0+lxpVDNbkSV5Z/RWtyfQJb9huKK2s9eBFAkdsYFLlkLBilKYTyOLny2unp7J8vzuz6WdbPG1eWfZrngKadXBF+PidXzjk+wWTeeC8jcn1xZbnmccWV5fsX667cBic3ljoo6+SGxuYTuaF5mctgXy/nYW4uSxEWPbaQgjQpcgf20V08BZDu9p3cgbiyryZ3Ejj7Q8CprwJO+jVgbl+2naCT64grL1p+JIsrIyGuPOGYJze1JtcWsSL8fI2nbLfVfE1q4yk54MprbCc3Na4cc3JT4sqmsHE5ua56X6AfV7b3LSczW8zNzy9ulGAiIvfgwf5nduyx2eNdd2WPpshdsaL/GR4+nI3BjiubY83TXdmcJ9cUuXKXW9YNObkpNbkukZsSV3b9zc1OiPby1JrcTZuybdx00+C+fWMX5ARqnzjKxgZJMhS5ZCxoIq78/e8Dz3mOf/sh5w0o5uSaDQanppqNK5tOreucXreTq9RgTa6c58zzi31TXevFTm7Z7squsZtx5bJOrtzwt0VuG2pyyzi5U1PZc/Y1jU/kdrvZZyffiarP/77rjRR29cw4OrkNM9TGU464sji5y4613NdATe5RTwd+6e+AmTV9V3fSEEwDTq7ElXsH7akVQMc46UhcWSXElVUFUwiZTYrMGLMtNP9/9t48SpKjOhf/sqp6X6ZHs6+aGWm0oB0kIUAIsQmx/CzxLDAPHtvRgWceFmD8AMk+5mcMtrEN9rNZbHP4YbNvfhYStpGEJCQBlhBaBqER0mgdzYxmX3vvqur8/XHzdtyMjIiMzKrq7urJ75w5VVOVS2R1ZkR88X33Xh4YXdmVXcmhdCIp22ZTcm12ZVuMpEw8ZSO5Q0P0Of8Wprb52JV1cJIGfcAz/W6ME04AVq6M73f66fR6ww30etJJ8e05pldPFMZtk20dGaG/hTy3j11ZxuT29qp7IauS64prlYsGPomnXEqu6XvfmNybbqLXW25JHhuIW+xNMblActLI11YouS1HQXILLAjIyWer7Mr33Uc5INglpMNmV25EyWUSwsmdZjPxlFTmTKSj1TG5g4PxuYIcH/W2yX3ZIdSMxFPcdv3aZOIpW0xuWnblZii5aWppo9mVs4zBJiVXP4a0K7sSmbVCyW2E5O6PwiplYtICs4CWlxASx01NPFUHqsKu7Irn1csAAUClDxjfRe+7hfqWUGo1JbdjCJgS1tc6251bYFceGaGHn8vgAHElV9paG1VydWJgI7lHjwLvehd9tmqV+bjyfRa7svwtGKyUSrsxx6cwfOzKABFAHoh0u7K+OGBSck3XzCT3+utpENm0SW17wglUdggwk1xpV77uOuCTn4yrnbJ9vnZlnSCnxeSaiGaaXdmm5KbF5LqUXKkgyLboMMVOyXa67rc0JbcguS1HQXILLAjMhl2Z+2udjDBsduV2V3JtdmVdyW1mduWjR5NjilzM1Rft5b7892+VXTkM1XzMFZPb1aUEA4msJNf0u8lx3Rb36orJbaWSK9umH4PnlC5nQCNK7tGj9r91M0gukPx7Fmgh5krJnZlQ6yRXJJ5yEWRZJ5dR7lPvu1eq91Yll0nuInVeILIrs5LbAruybkdOI7kusuaj5KbF5N5+O60w/+3fAhdfrPaX+8r3pZL5e8DPrmwiuXmyKwO0Usu/hy0mVyeCOpjk8n7r1tHf6Ngx4Kyz4u2XJNdkRZZ25bvuove/8zvx87nsyllJrm9MrlRDmx2Ta/ue28b3dFaS6xuTCxQkdw5RkNwCCwLNtCvb+h3uE9NI7nxUcn1Irk5KXGqp3I7blqdOLi/S6t+bSK7Jrmxqm6ncYjPtyvU6tSMtJjeN5PLcgMdQ/b51Kbm8INFoTG4zSa5c2AbM7gGfRGZ5ldxqlean11xj/r5ZJLeIy51FzFniKS51U8LMNIlLCAUVoNwT3z5RQqgu9o/QIchGzyr13hqTG3XanUNKQQaUXVkvLRS7LoeSmzbxt5UI0kmuza7cSHZlU0wud6CXXZY8rs2uDJiVPV3JDUM18DHyklyTrXdgQHW2aXZlm5Kr25WDQKm555wT35btytPT6XblyUngta8FvvOd+DFcdmVTTK5uT5e/eZ6YXD1Op5GYXGlX1r/ntvHvk0Zy+e+nt91E0Bk2u3IzSO699wIve1kRQ5OCguQWWBBoZnblNCW3mTG5YUj/GlFyXYmneD7TDLuyTaG22ZVl202ZoVnJ5XOY7MoSJiXX1Da9pGIjSq7p7yKVWldMbne3WeHmtvO4P59ictNcBy7oSq7JriwJeLOVXO4D/vEfzd83QnJlfdxiTjGLaLlduUqkVT+HS8ntXBR1Rq4auxa7MkNXcmN1cDUltzOyK8+s8EV2ZR8lN6ikx+TKyX0eJddlV9aJsEvJtdmVZcdgO65871L2qlUVk8sdQbOV3K4ute/AQPOUXAkXyQ1DGkBNmZOlkjs8nBxkTe1Lq5Pro+TKWGpfi7mvkuuKyZV25UaVXNO5+bhpMbmtUHLvu4/UeLkCWyCBguQWWBBopl25USXXRgZNsam6qqdPoKWSaxpX5Tb6e26Tr5Lrsiubzp2WeMpHCZYkVx7fV8k1JZ7S7cp5Ek+xJZh/NxfJtcXkNmJXlmNlK7Irt6KEkK+S60Ny8yq5gH3eXyi5bYiW25VrpMoCGpEWJLckSO7UUYqR1bdPKLlM5oRKyHbloAx0LVWf25TcIOp8OhbR97VoAGK7sld25c7sJYRs2ZPTlFyfEkKumFybXVmSHdtx5Xv+bvlyYMeO+LVPTSm7claSm1ZCSCqeZ55J79/97nSS6xuTe/Cg+oxJ7tlnx7c94QR6PXQoXck9dsyP5PJ1SdtVFrtyT4+daLqUXD0W2BaT61LuXUpuVpJrs+iVy2Y7Gtd/BFpDctNUmQIAgEr6JgUKzH9wf1gqtS67crPsyrr1FVAVG2RCR9kmLiGUNSZXxkDavm+Wkqv/LmmkRZJc/fujR4FTT41vLxeBXUquya6cNfEUW4JNv5sce5sRk2u6r+T1pJFcffz0yWA8m4mn+LdLK/uUtqiSBtOzYTp+oyS3UHJnEbOh5FZ6gNqwxa4cIGZXnjwAdC5OtskWk2tScruXK+LM25hicstCyQXIstzRTySY6+RasytH7Sl1xgk00PyY3CyJp9KUXP349bqb5LrsyuefT2oXIwzpeGxXtpFcJqONxOTedJPKfviNb9DnNruyr5Ir7SRXXQVs20bXKJFGcmXiKf3vyTApuaym8m/F1z42BixdGt9X3k9jY+p+AuaXkpvVrmyb2NlicuXxWmFX5vakDXzHOQolt8CCAI8vAwOttytnJbmu2FRJDPr61Likt8lXyW1m4ilJYvMkntLdPFntyq7EU/qxTXblRhNPseVXXqt+fJdd2ZfkMmwkN21hwpV4yhbjzc6zVpYQssV4u+4HvqY8duW0sV4XTrKgUHLnCLOReKoUrYbFlFlBUqVd+ehWYPB0ta88TvzA0askudFkWlqVgSjxlEvJjUguZ1iuT0ZWZoeSO76bSHXHgGgLH19Tv2RnMzLiT3KzlBAKQypLsHev2tcUk9tMu/L55wOPPaZIiiRNnZ2qvc0muTww8CprK5TczZuBf/7neJuAOMl1JZ4aH6cFDV+7Mu+3eTO9btlCrz5Krv59WkyuieTqfyOGTnJNSq7Lmg/kV3JNdmXbwN0KJZePUZBcJwqSW2BBgEu69PTkm4RKZ0leuzLvZ1LPALeSWy7TWJSm5OZNPMXzAxOp0e2lemyxzKsgxwFJgm125XKZ5iNpSq4cGzmkyGVXzqrk5iG5PkpuI4mnXCS3WXZlU0gaYHcGNMOubIvJlX+ztOzKeezKaW3l7/OUASqU3DnCbCSeqkTkwqTkypjcyX1UAmjx2fHty93+JYSAeNIpAIBFyZXZlQGVYVnalW1K7uh2oO/EZOZmwN+ubEo8JUlRViX3Xe8CPvpR9bmPXbkRJfeCC+j1/vvpVR6nq8tOcjs7iZi5SgiZVuGkXVmCSV4zY3JtkCR3V1SuymRXZsLso+RKq/b559M9cOutqu1pJYRcSq4rrlVXcoFkx+2ToMJWJ3d0lD7j+7URu3IayW2lklvYlZ0o7MoFFgSY5HZ351NqZF/Llk7uGxmzoeTqJFef+OdNPBUE5nHZ164sv9MJULlsTzwlxx9fJXdigo6VRck1Ka2NZle2kVzfmFxOPNWokttI4imbkptGck2xyGnQ55j6b6cv2rtIbiuUXH2hQhdBXBgZob/nxESh5M4qZqNOrikm15R46nCkXg2dHd++3JNUck3ZlWfsygYld1p0EqbsygDFA/P3bFe2Kbmj24He9WZLc5pdmUmRJJK1Gt34jZQQknZbn8RTPBibSK6vkgsA3/42cMopqsPVE0/xbyExNORWcjk7s4Rc/ZTg36PR7Mo+YJL7jW8AN98MvOpV8ePyNRw4EG+ThEnJ5WuqVICXv9yf5OpKrismV1f25e8p/97y76VnCteJqqtOLrfdljSK4aPkmrIstlrJLezKXiiU3AILAlLJzUNyub/g8cDU9zABtR2/kZhcG8nNouSaSI0kRDZLsU/iKddCpUvJtcXc8v4mkmsrS2dTcvkYUhAAmpN4yqSAZ43Jtdm4dZIr76tGSK5LyU1bNGmFkmuzKzdbyc1CcvWwgDRMTan5fVsquVs/DRx6cK5bkR1zZVeWJYSYqB6Ofr8EyXUouSa7sq7kJmJymeTqMbkGu7LNkjD2LCm5pjJDLruyLSaXra+NlBCSHZxPCSHdrixJLp/fRHK5XUuWAM97HvDlL5OKrNuV+UE2WWHTSO7ixVSqR0LalSVcSu6VV6qaZzYlV/4maVgcxYv/539SPd1/+7f4ar1Ocn1jcuU1vepVwFNPAY8/Hr9fgCTR1JVcm13ZpIaa7MppSq4r8ZRJye3rMycxY6TFgvFxTavhs0VyCyXXiYLkFlgQaJTkylhFwNxvuOzKYZhUGRnNiMl1lRDiz3p67EoukE6C0xJP2druKiGUR8l1kVybkis/a0biKZdd2TcmlwWDVsbkpim5We3K8m8aBNnG4LSYXJfFXG43G0puHpLLokdbktxf/SHw7HfnuhXZMVd25RnSKe3KB4GuZUD3CrUvgETNXLm/r5JrLCEksisDKiaX7cpBgASBBSgL8+SByK5sUHI5kRDgT3KPHaP3rsRTY2PxGrH6MeTAHASKkLF11haTK1dxJfSMu3oHBFCJlcsuA3buTNqV9TZKSJIbhsnsykuX0vXKa7LZlV0xuXfeac5KbcLate7vAbo2/vtddlmSxHLbsii5+rVfcQUNrh/5SD4lNy3xlMuurA/kekyuPqi4lFwfknvokHrvsiubVsNtduXpaftkMQsKJdcLBcktsCAg7cp57ITcXzAxMvU9LruyHGtboeSyWupScm0k11fJtSUKSgs5cSWeyqPk8nzKZVd2LQBnSTx1zTU0ZuvIYleWYgNA9x8vPrtIrj6naUVMrlx8kcdKsyvb7M4uzCcl17TQ1Swlt+3syuE0gJAUwHbDbCi5rhJCQRBNsKNJ9tDZYsIt7Mp6CSFM0z5SRevfBKx+HbDylfFNjSWEBLmW2ZXDMLIrd4JieQ1K7uiz9GojuZLISqtmrUadW14lVyc8+jFkB1cqARs20PtnnqHXtBJCJpLrsisDpOaefDIF1etKrjyPDkly9RVwQGUUlsmgpqbiq64M/k10u/Lhw3G12EVyDx4EHnnE/r0ETyJe8pLkd81QctetA/7kT4AbbqDffNmy+L5ZYnKzJp7S7/fxcfW72ZRcV3bl3l43yZV/Xx+7so+Sy9eVx2ImUZBcLxQxuQUWBJjkmpQ1H+gkN6uSK/vHvDG5/f0q+aR+LFZyTZN37uO6u83n5j68EbuyKeREV3J1MtcKJddUJ9dHybWNJZ//vPlzV3ZlPfEUQPccbzsyouYOnZ2KtDNsv9v4uBqTbXZlSUL5GK7synw+vfJDGsnlcTtPTK5P4in+m8jY90aVXLn9kSPJOeNxq+QyGZtut4ZjdmJyS51EBm2Jp+T/F58T3xcgu7JJyQ00DaHSA1z6H8k26MmhwiqpuDP1PbspBnfqiFB5u4BgVLRTYHQ7vfadCBzbRtvIB02SUTnB5wHOlHjKRHJ14qFbV/Xj60ru2rX0/TPPAE88Qd9La64sIcTWktjvptmVZQ1eiWXLSJHj8+skl+NYJYaGKDMzkFw1BYg8A0QWWWG1BfrrSi6r6I8/Ht/ORLZdbUzDxRcnP8sTk6sruQDw4Q/T3+Tkk4HXvz6+r80ZANgHNxkTlUXJlRkqTaRR2pXzKLkukisHNFMSC9kWE8nt7qZn0ZQAxgeFXdkLLVNygyD4ShAE+4IgeFh8dkIQBD8OguDx6HWx+O66IAieCILgsSAIXiM+f0EQBL+Ovvv7IMhzNxRY6GCSa6slmwbZ7wDmfsOl5JoSBurfNark2kiHjCdutV3ZtFDZbCXXRnIl8TEpufydL8m1JRCTbTONXXLOw+eQxOfYMTUXdGWdlm1kcNttJFdXS9OUXL3tac6ARkiuXHjgc8jzm0KYTItDzVBypUCiHx9ojOS2n5LLD3QbktzpWbArBxX6Z0o8pRPVxeeq95vfR68nnE/7SpXJRHJt0JXcOiu1Ap2LKCaXFypc2ZWZ5HLiKWqQ+N6g5NbrSZIrSayPkiuTVjFcMbmdncDq1cDWraQ6Dg0Bb3ub2kbG5OoqLh8jza4MKKVxzx56lauTALB8efLYUsllQigHJFZy+TvATAYBsgr9wR+oc/b3E0nm5E2twsaNyc+4fZwu3je7sj5QdXTQNV1xRfxvY1JyXSWE5D3nSjxlU3KPHYsr5K7EU3licn3syj5Krrz39clm1lgq/fiFkutEK+3K/wLgcu2zawHcFobhZgC3Rf9HEATPA/AWAGdE+3wxCNirg38A8F4Am6N/+jELFGiY5KbZlet1Nbk1kaO0GEqgsezKNuUNcNuVXYmn2MqaRnJtiafk9yYyl1fJ5XweixfHt+/vV+TE5BRy2ZXDMDk+/upXsMJlV5b5VaSSy5CVNmx2ZZ3kMpnme8tmV5YLC/zqisnl8+nH8lFys6qpNiXXZlfW29bMmNxWktz2U3KjH6adldyWJp7qSJLcmVhXbU19SCi5694IvDUEetbE2zrz3pfkmpRcjSx1DFF2ZZmUypZdeexZup6e1YrkhgYFC4g/iHqd2zS7chYld3Iy/rAzQd6wAfiP/6DMy1/7GnDmmWobGZOrJ53iY6fZlQFFcp97jl51JddFcsMQ+MUv6DPO1gwoJVe3K5tI7gtfCHzmM+r/QQCcfTawe3dy22bglluA73/frA6yIp5FydVt5C7oK6NpiakkyXXZlU1Krl5rUCexDz9M951NyfWxK8vJno9d2RaTa1Ny5XGyolByvdAykhuG4V0ADmkfXwHgq9H7rwK4Unz+nTAMJ8MwfBrAEwAuDIJgFYDBMAzvDsMwBPA1sU+BAjPgRVQT2fJBml1Z9nUmy3AWu7JLydUn3z5Kbt7EU2mkRBKqtJhcTjwliWReJZfnDTyPYAwMqLmWKyZXV3JtYxiXTzTBlXhKKs0mkisXl23ZlblN3EYm9Hyf2RZN5MICkF/JbYVdWVdy0xJPyX3kdq573YUsJJfvI1+0d0xuGyu5LbcrRyS3VCEiXT0G3PlbwPURcdVJ7uBpyWNwgqhYGaEMSi4MMbkljdR1DpGSy3/DUqTkmkju1BHavlROktx6nTorJrJMHqtVd8fpIrlSybWRXH31VpJcPu+LXxzfhjsoG8m1ZVduFslldfu//ot+r7PPVt+blNwsdcnOOku1pdl49auBq64yfxcEdO08iPkouRMT/hmeZccdhkkl14fkmuzKJiV3fJzaaFJy77iDfuPt29OVXJsVmq+d4ZN4KouSyzb2vCS1UHK9MNuJp1aEYbgbAKJX7l3WANghttsZfbYmeq9/bkQQBO8NguC+IAju2892jALHBZptV9aJgyS5ee3K5XKyn9VJLodo6O3yKSGUNfGULVEQf+6beIqV3DBMfp9HyT10iOYKekzlwICKb3WpgiYlV287ANx3X3w7vW22sYvnB4ODal85FuokV190kX8Tvt845MpEcrPalXlhw1W6j/9mtuzKvH+WRea0mNwsSm4eu7Iek2v6nuczWYgqJ1dt2xJCCyEmt2VKbi2u5D7+T8CuHwL1aFKqq2Flg1JXim70mN15WiWOSoMpu3JCyV0UxeRGnQlnVzbZlaeF3ZnbwL+jbknm19HR7CTXZFe2kVx99ZY/P/FEej39dCKWEpzVb3LSruRmsStLkmtKIiWxKirz9MQTwN13AxdeGLflcoctlVybXdkEJsx8/bMJvnaucafDNKD6knc5qDCxk/dEbzhOXbYAACAASURBVG/8XhgZoTZUKkm7cpqSq8c1ycnVZz+rtkvLrqzvK+EiuXJAM8U1pSm5rnqVPihIrhfmS3ZlU5xt6PjciDAMvxSG4flhGJ6/TGZ8K7Bg8JWvAC94QfJzJrkm5Yy/f9GLKGO/CWlKrlyIzmtXNqljeuKpMIwv+ukT/zx2ZVviKX1O4JN4yqYq8lipE7I8Su6hQzSH0OeXg4P+Sq5c/LWR3G3b6HVy0lx+z3bdx45R2/r7zTG5w8PxxFM+dmVWcvlv70tyTb8rk+hGlNxGsiunxeT6LJq0wq5cr6uQwSwkl9vRvkou12RsY5LbSiU3qCgld0LL/OczRQoMSm7WmNxp8SCYlNxKP1AbVQsVnF3ZNB2SJHem/SIDMqAm9/xAjIwkSa4kFvp+QFJdGxnJp+QCZOnVwYPK2Jg9JjeLkrtrlzquJHemY7/ylfR6/fXAli00eZCoVIiU60quL8llJXcuSO7KqISVyaoMJMlmFiVXWun4by6V3GXLVDwwb8P3oK7k8nFYHQDiAzWveOtK7u7dZIFn2OrkSpXZlriDnwmfBBZANpJb2JVnBbNNcvdGFmREr/uiz3cCWCe2WwvguejztYbPCxyn2LKF/ulIU3IPHwbuuQd44AHzcbPYlbNmV3bVB9WVXCA+H/BRcmXbXUqui2Dz9/J4vkquTC6oh6TYlFwuFWdSJA8eNCeTZLuyVIxNtlx9TLaRXNO4I9vusisPDNC50+zKaYmneH/drmyLyTUpubaFjUZjcptdQshULcI0J2hl4il+xrIQVf779fRQuwoldxYxG3VypZJb1VKh8wT5lXcAV+5I7A7AruRmiclNU3JLHUTIpV05CMwlhGJKrmZX1pVcF8mVxIJX32THmkXJ1Ukuf84k96KLktfBhHF01G5X/uY3gT/6o3g7dJLLcS82u7IJq1YBz38+8Nd/TR3Hy1+e3Gbp0vx25TPOoPafeKLKLj1b4KzLepIwRqNKLg/QpoURE8nVE51JJbezU5TxQvx+tym5t90W385HyU0juf39fnZlnzq5zSa5hZLrxGyT3BsBvDN6/04AN4jP3xIEQVcQBBtBCabujSzNw0EQXBRlVX6H2KfAcYjhYepb9H4hjeTyZ7YJalq/w2P04sXZ7cq+Si73t9LN00olt1GSa1JyJaGTZE4nLfpcS1dy9XhcgIhlrRbPYWJKsKSPyT4kV78vdLuy/N2OHVPjqk9Mro+S67IruxYmXImnGo3JzZt4yhaT60oWJrdrZeKpPEquTPKZtw73nGIhxOTmtSs/9TXgMUudMEARShvJZTPZipcBvWsTuwMQMbmyjQ1kVzYpuaUOaqtMPIUAMXIc2z+F5LKC5UNyOTNyV1ecQJoST9myK9uU3Je8BPjgB80xpGkkt1Qikvm5z6mYAiC5baVCHazJrqyn8Jd43evoul/1KvqnY8mS/Hblvj4i0O99LxHdk07y268ZYJLLyraORmJy5Wq3ScldupR+M9PCiCnxFB/PZVfWldy776bJAtvQTUpurUYdO5/btqrKz0Rvb/Ptyj4k95577NmXCyXXC60sIfRtAHcDODUIgp1BEFwN4NMAXh0EweMAXh39H2EYbgXwPQCPALgJwPvDcKbXfx+AL4OSUT0J4EetanOB+Q+2q+qTc15ETSO5tqRUvnblpUvnn5JbrSo1tZlKrk465D7ye5eSK+3ItuRN+vdsV9bBdtFjx8yqoG1MtiWekuOOiYi6lFyd5MrSP6OjcbuyqzaxT+KpvCWE8sTkzoaS6+MMaFTJ1WsT8/G7u+n4JqL65JM079TFMb0ucqHkziIaVXLveSdw/zX276cniRDKxFMSPhULA1ZypV25noHkluOxtTG7cYQZkitKCAUls5Jbd9iV9dVF7qh8SK6eJKEZMbk9PcD/+T/mVU0fuzJAk4KDB+1p+QFSEE1Krh4HLPG2t1FG5S98wXwfmJRcX5ILUAkek0271XjpS+nVNhkyKbm+JFfGLdmU3Hpd/a1MJFcmnuKJhcuurCu5HEMtP+fj8zVx26Rd2TTY8WSio8OdbMVlV9YHHB5A0mJyf/lLssl/4hPm7wsl1wutzK7838MwXBWGYUcYhmvDMPz/wjA8GIbhK8Mw3By9HhLb/1kYhieFYXhqGIY/Ep/fF4bhmdF3vxdlWS5wnILHShPJbUTJ1UmuLfHUsmXm7MrNiskF4iSXt+3ocCu5/H0jSq5NeTOppfK9rCfrq+Tq45P83mZX5gXb4WG3yuxrV5b3gknJdcXkclv0mFy+P7MquXpSo0YTTzVDyc1KNPV5QyOJp/IouXJ70/yN/6Y2NfbKK4GPflTNhfVjta+SW5QQMh87TCq5tWNAWShPPlMkk5KbKfGUruRWVZzvzDYdRH59siu7lFyfmFyefEsSayK5knjU67S/TnL5GDYl1wUfuzLjyScVcTINHsuWxZVePrZLyT3tNCIap5xi/l5XcrPYlecSaaqxaUDNYlcGaGBhRUInuYCyLLvsylNT2ZXcY8eoNuCLX6wGVZldedqy2OOyK3d3mydXvjG5AwPxSSM/g9w+2yDLg/O3v+3+vlBynZgviacKFPCCS8n1Ibm2xUvdQdKIkuta8Mur5DKRtCm5NpIrSwjltStLJde2UGlKPOWj5MrxyceuDCjLOu+XFkLksivzHM9mVza5kExKLu+v58Lo7KR99ftDJ7m8Pd+fWWJysyi5rYzJtZFcV0yujeQ2ouR2dZn7gDSSK+8/iQWj5LazXbkVMbmcKKrcFVdyu0VJmSxKbu7EUwYlV8/ibLUrI6nmyv19Y3KHh7MrufJ7k2ont7HF5LrgY1dmPPUUPbgdHXF7LENmUOY4T8Ct5KZh6dJ4fGkWu/JcIgiAm28G7r3X/L3snNkGnseuzCq3TALrIrkmuzL/nr5K7ugo3Y8vepGaMMg6ubqS2wjJNcXfmGJy+/vjJJffp5FcHjAff9z8faHkeqEguQXaCnlJLvcHvnZlm5K7dCkdw7aoJ4/FyBqTm1XJlaqjibQ0alf2yYZrsiu7lFyTXblep/5/YsJtV5ZKbqOJp/j8OnHJale+4QZKiKZX2kj7XWwk1zcml39XvZJGXiXXda+mQZ8/z1V25d7efEouuxxZEGLwsbq6aN+2I7lMENtZyW0JyRWEUcbkdsnKDB4kl5XcWBszJJ5K1Mk1KLmlTrNdmU4c35Yt2EBzYnI58ZTLrqw//DPtTonJdSELyWUl15SWHwDWinjqjg41ADVCcpcvp4kB23ey2pXnEpddBlxwgfk72TnLzs8HcsDbF+WVlXWIfZRck13ZpeTyYMvtBkiF15VcmXhKfw5cMblpSq5tNVySXDlo+Cq5chAzDToFyfVCQXILtBWYROiTWElya7Xk4ravXZn7cpeSCyQtyy67clYlV4YvSSXXRjrY1WP6fnRUzU0asSvnTTxlU3L1RVj+Xdj95RuT20jiqclJRS7z2pX5PF//OvDbv63GbqnkAnYbt05y89iVgaRS7BOTa1sU4eNmJZqmMCd5TtfChKlteZXcvr58Si7Pd2WYHaD+Jqzktp1dmRXGdlZyG7UrTxtuCFmOJ5BKbkaS26iSW+4C6qJAui0mN5RKLtuVkayV6yohlCe7so9dmQetrImnXJAk1xSTK4/BSq5p4ADiFt2ODmDdOnp/+eXp7bBhzRp65QRO7WJXToMcMLnzy6PkMsmVtqysSm5aTG5fn7o35KLHsmVJJVcmnjLZlV0xuY3Ylfv6zCSXnxUfkmsqKVIknvJCQXILtBV8YnKB5HPfLLsy99G6ZbmZ2ZXlfIDby8qbrtoBRPwHB5N25vFx+sdjTJqSGwRxEs6/dV9f/sRTaUqubldmq6hvTK4t8ZSca+ihPty2ej2poOptL5Xod7EpuXLsf+op4NOfjrfVl+TyeGxScn1ILn/O90azYnJbpeT6EHDbuX/6U+A//zP5OW+fV8llkisdiEAyJrdQcmcRzVJyq8PJzyRhLFWIROpKrg9RtZUQ8iW5/ScBtVFgIiIFXNYodo7IrlyXxNym5GaIyeVBM61O7vh4kuj4KLm2mNxW2JUPHzYnnQKAk09W7zs6KKHU008Dv/u76e2wgUnu3XcDr3gFBfO3i5LrghxU+J7Io+Tu30+TD7lAwSoBd7IjI+46ufx72pRcWeuX9+/qovuQj2tScrPYlbu63HblIMhGcn3tynJwfvDB5PeFkuuFguQWaCvY7MocDmMiW/L/tgkqT2RZhbLZlfVSLwwfu3KakmtKPMVKrG2xEKByKYsWJfthnTCmKblA/BisaC1daj633D+rkmuzK3ObG4nJPXQoPtcxqZ08dqfZlYH4bzI1RfvqSi4ArF4N3HQTvddJrs2uzPvrSq4tJlcuLMhX3+ROrU48VSqpa25V4qm/+itVGlOiUbsy3wsuktueSi6TXMsK33xGs5TcmovkRkpudZjOl1nJ5cRTWnZl3+nVQJTYaHibapeu5AYddMx6dPOVu8y1Q2f2jzoWTn7Fv6OJjA4MKJIrB5osiadcJLdcVgO3/DwN3JHU63525SxKLkA1en3aYQOT3K99DfjJT2hQW2gkt1ElV1qVAepABwf9Ek/JgZy/u+46tfouLVVym6VL6e9qSjylK7nSdmQa7Dge2ZXwRJJc02DdDCXXVi5AvhYwoiC5BdoG1arqK2RfMj1NzzmXENK/l/+3Kbn8OY/jer/BlmDul3S7sm8JIb2v5OP09JiVXLnQaVOpjx4lFYqPzXMetv4yYdRJDU/W5bisk9yBAfpdbaQkCOJl4nyV3GPHaF99IdXXruwiTHv3AitXqv1MbU8juTJplvxNdJu1JLm/8zvJtma1K6cpubzwwGO/fm2+DqpWKbl9fWre2KrEU7If0D8H8tuVeS6URnLbVsmtT5pLzsxnNE3JNUwSpSpaqgBT0QpbTMn1icm1Kbme2ZUHdZJbNduVAVJ8gbhdWa+VW3couaOjdE2y4+rvVyS3uzt+zaVSYzG5AJEO/aHJouQCbrtyuUyW4d277Uruxo3m4zaC1avp9e671WcLwa7Mg/noaONKrk5yAbLD7d+vSLTNrnzgQLLW7Te+AXzxi/ReWqrk/my340FYfs+D0M6d9LpiBb3aBru0xFN8H/NiTha7chYl15TttFByvVCQ3AJtAxmraorJd5HctMRTvL2tdFmtRsfm73X3lUmd1f/Pk3v5vYxj7Oyk7+V1SpJrWiwESMllkguoflwnuXo/zf08L0jr2+zfr8YYW0wun5P7a1ZogbjbyKTkDg7GnUS1mtuuLBOBmpTcWo3OeeiQGrtsbed7xhWTKwk6/+Yuknvuueo9H9dWWomvm0kuk8O0xFNPPkmvLEzYSK4tjjotW3ejJFcmNm1V4in+O+uoVuk37O42f8/3q43k8m+TZlduPyWXO4ywNQmcWokZkmuI08gCl125HCm5THJlduUsiadianMGu3LveiKlx6SSa7ArA0B9VLTZEKfI+89kZzbE5MqVKCBJcmPnjcquTEy4syu7SK5ccWRkUXIBs5LLD+app9JvsGePXcmV12U6Vh7091NHLzuEhaDkdnWRnfvGGxtXcmVmZQaTXFNcLKAGChPJlW1xKbmAmpSwkiCV3N/8hr7nyU+e7MoyqyeQ3EbGz9Tr6vjj49SWtDq5chArSG5uFCS3QNtAOp7kc20iufok19eubFNyq1Xqw5jU6MfPq+TKfj4IaNzU7co8BtiUXGlXlt+nKbk7dtAr5+AAkkqui+RKIsjzmL17zW3XxxB9EXbxYmovEwyTXZnjlvWYXGmL5VwXvkpuWuIpIP6bmGKJAeCii4Czz1b7u5RcOTbyeN3VRdumJZ568sl43hRb2SebkpvFrpwn8ZSc4/oknsqj5NZqdiWXS2DmsSvLxR2JBaPkAu2XfEpmHQ4tN4QP0mJygwowGZHcTkGUfIiqnniqNg7UxvxJbqkMDJyslNzQoeRWoxVQWUIoa0yuTkT7+1UJIRvJdSm5Lrsy0DqSy+TlrLPUZzaSK9EskgvEV4iBhUFyAeDtb6c40Pvuo//nza5sUnKXLyfVXb9npF05DGkCwiRZKv+8kmpTcnWSy+cpl4GHHgI+8hHg0UcpA7OpvJBEGsmV7bIlPeH28sAxNkaf2TJiMuTgrFsHgcKu7ImC5BZoG2QhuVntymlKrpxAm47vW0JI70v1fn5gIH6dPkqutCvL8+skV48n3LGD+ng5TktiI0muLVEQf85j2Z496nvuy3l/W4ZigNqwdy+wfTsRXlOpQ0D9PjZVkEm2VHJ53sMEGEjalfX7whaTq8cSA8ATTwC33Qacfrr6jO/DNLvy4KBa3JDkzBaT++STFEpmi8n1VXLzlBCq14F3vAP45S9hhE3JdbXNll05j5LL92NnZz678sJVcsUP2W7Jp2Ikt4HJXFpMbseAOn6HeLjzlBD64UnArhv9SS5AcbnDDiWX435rTHI9syub7Mp65+qj5PralfXsykBzSK7JrswP4plnqs9sdmUJqb41Cp3kLgS7MgC85S30O331q/T/rEru+DhZqkwk95xzSEnlAdmk5B47Rh22SckdHVUkWJaA0pVcmTkcIGINAJ/5DFnM5YCdp4SQtGTxMWwxuYAaSMbG6FlKI7m8faVSKLkNoCC5BdoGptI6QJzk2khoo0ouT5BtSrHLruxScvWyKwMD8RwDaTG51Sr1+S4ll0ne4sWk+nJ7duyg+Ydc2NaVXF5INZESSQQ7O4lM6yTXV8lds4badd99cWVZB/8+NlWQzy9J7ubN9CprqmexK0tbL/9tJEE/6ST6+5nmN2kk961vBW65hcblrq6kXblUSpJcmUPFlXgqLYMxk1y9xi4fVx/39++nUkm33Za8TiApErlicl1tS0t65bIrM3m3keByuTGSWyi5s4wYQW+A5KbF5A7KFSoZy5ejhNB4NJmeOmre3oSBU4DhJ4DpekpM7ggR11LFL7syDCTXpOTaSG4Q2EkuQA/rpz5FCYEAs5K7apXtqt1IU3L5Id64UQ2gLiX3zjuBD384X1tsWKhK7vLldG08aGYluTwQm+zKF11E99Sdd9L/TUquzHopvwPoXn3mGeqon/989TkPMLyqryu5f/RHwPvfrz6TJDdvCSG5YKLH/+gklweO8fFsSu6iRW6SWyi5ThQkt0DbQCqcchIrKx80S8k12ZV9lFy93AwQzzRvUnJLpXiWXXmdck5iUnKZdNmU3N5eNT6dcIJaJAWI5OqEMotdeXIyPvdYuTJOcqVooI8RJpILkJto7VpY4avkSvFg7Vr6DSTJzZp4av9+imE2KbkStjmPLbtyXx/wqlepbXW7cleX+t3CMJ3kpqml3A5Z9kn+TV0xubxoYhpvgfSYXB+7cqVizuEhMRt2ZUn821/JlVn62ozkNkvJdcbkdgGLzlCfd4gVLK/EU0LJlTfO+C7/9nWvoPbUR90xubURlTk5i11Zj8mVyKvkAvT7hKFKpuBrV/aBXDV0WYz7+4FNm+i9S8m95BLgs5/N1xYbZEwnsHBILkCDHKutWe3KXDvYpOS+8IX0euut9KqXEDKRXPlcjYxQHTkAeOlL1edHjtArL3ToJPf1rwc+9zlg/Xr6v05ym21XtpFc3a5sG+h44Fm0yG1XLpRcJwqSW6Bt0Ay7ct4SQpx4yqbkMjmQpER+J5MY6Upub6+aS7mUXFOMJffrJiX30KF4bCu/Z7LiIrnj4zQ2uEju8HA8gaEkudUq/bNl6DfZlbntLpLLiwAmwlSrme3KpRIRQxPJtSm5ul35hhvotzIpuRK/+U08LtmWeMrkmJPkTJJcvncPHqTzN6LkMhEMAvOz4kNyTeMtkFRyefz3TTyl/+ZyHwkfJbcRu3K1Gn8GdaeIbaFs3qKdldywjhkyl0fJZZU1za48ZCG5PlMkqeTWRt3b2lDh1dUxcwkh/n9tVBBYg115uk6/WZaYXFlCyERyJyfpQbQpubHrMNiKmeRmjYVNU3IZvb2K5PrE5DYTGzbQ6wUX0OtCsSsDNMhxp5hVyXWR3CVLgFNOUSTXZFdmOw1PQJ57Tu0/MgL87Ge0sn+GeG71rJU6yQXomXnNa+h9oyTXZFf2Jbk9PeYBWoJ/+6Ghwq7cAAqSW6Bt4GNXbjS7MvfltsRTaUpuR4c5u7LMIqwruXrJQltMrsmuzCTXpuRKkst9/6FDtDBqI7mmhVRTf+wiuUyEsiq5QLqSKy3XbKvltu3ZQ2OzPh87+eTsSq5cmGDoJe5M7ZPjusmuzInCdEgrLF9fd7e61/TMykA+JZfvozSSq6upaSTXJBLJv7uvkiuvy7TIXavFk1Xq++dVck2lmoC4kmuL953XaPeY3HJEHBpScg125WlhV+ZatUB2JbccDRr1CWAyunHWXQVc/D3/9pWjjrI2QoTUpeTy7wGDXTmMbs5mxuQySfAhuSawXdlmf7EhLSaX0durOsXZJrlvfztw110UZwo0N953riH/XlmVXI5/5QmEjosuUgOJj1356afVvqzkvuQl8fvPh+QCwDXXkG1ZDqR5YnJN2ZVdMblZ7cpSyS3syrlRkNwCbYNWJp6amorH3NoST6XF5HZ2piu5evkzOecw2ZVdiafYPitJrlT+bEru4cN0bpuSq48xJtJhI7nf/z4lYwLii7RHj1KZnSNHVAkhxrJlqv0uknv++cCvf03JEYEkYdq7N67iMjZvJpLIJM4nJpfbY8rk77uwzXMDOUbt328OVWJydvfdarGgu5v+f+aZ6jfVx2ZuL+AXk8ufZ1VyeQ5hsyvr9zK3zzcplm4R17+X2+nt5v83Yle2JbM0kdy2KjcbigtrhZK79S+AO/6f5h8XiFTJnCQ3FCWTTHblulByy2ISX+oSNl8PkssxvLVhYDJSoDa+A1j/Jv+2VqIHp3pUtUlC1slNKLkyqF5cE73hL+jFZlceHaUHOCvJ9Zlgs5LbCMl1Kbl9fcB551H7TJ1/K9HdTZZZtkkPG+6zdoX8e2VVcnliYrKvA8Bv/ZZ6b6qTq09Arr6a2rBoEQ1Ejz5KkwEJneTqiacYZ50FfP7zcYJqio8JQ5oY+Cq5vjG5WRJPcUmJPHblgwfVyvhxjILkFmgb2GJym2FXrlZVnVrAnHjKJybXRHKlPXXZsniWX5OSy1ZJTlrpKiEk7cpMnNgue/BgfGFbKrmm8kFAkuTqiafe8Q4aHwAzyR0bA978ZuBtb6PPpJILAL/6FXD//fR3kGNoqaQW/F2Jp977XvodPvc51S5fkjs5qWoD63blqSn6rX/1K3UsbvMzz6jj8DzQR8AAyJVVKgEPPED/56SQpgVuTjx12WXA3/6t+gwAtm4lhxagnHl8/dxeoHEl15VduVElV1eJ9baZSK5NyQWSz3IWu3K9bn7G5bEYOsnVv5/3aLWSe+Qh4OAvmn9cIK7kZrUry3hel125rKlUQZAkki5Uosl0bUQpud2GVSwXyhGB5GRVruzKekyutCvXNZI7o+RGv8XIiJnkAtQxmRJP8aqWieTaJugSTHJtMR42ZLErv+1tpPZlJdLNApPcw4fn5vytQCNKLk/WbOT49a9X721KbkeHmmBccAENPKecAjz7LH2mD/Q8cUpTck0w2ZVljeBWxuS67MqdnbStaWWZByHbQtMf/zHwuteZvzuOUJDcAm2DRuzKPkpuR4ddQWK7clpMbmdnfN8f/YgIFfeFmzZRH83tsSm5YZi0xqYpuUx+ePHOpeT+9V9TH3vuufHrSFNyAeCmm+jVRHIZ/DeRMbmMrVvpVZ+PsGXZpeSuXAlcdZUaQ03ZlU15Tk4+mV7Zsswkt6eH/qa33kq/87nnAo89FrcrS4yM2MsbmTAwQCLDXXep/Scn7Uru2Bhtw4sXUnH80Y+A1avjc828MbmAW8k1ZVf2IbkmJTePyuyyK/M+plrYvkoukFRzTcRWvpeLXG0Vlxu2mOROT5EC2Qp5uxElVybcSrMrA8Dl9wMv/ha9Z1LpM0UqdxEprQ4DE5GS22WxaVqPkUfJNdiVZ4g7bxM9SOE0dTx79iRXEV0kN03JZfzDP8STEUgMDNC+WQmotCin2ZVLpdlXcSUWOsnNquSmkVxWZYF44qkgoI6YV4L1Rab+fkVydWv6d79Lq+BsdeLjnndeertNdmWZ0dTXrmwiuXqdXE7i5qPkdnTYSW6aknvgQDwT6HGKguQWaBu0MvFUmpKr25Vt9UWlknvzzbSQ9oUvqONu2kTbcj9tUnLrdeoHmdT7xOQuWkSVFADgqaeo/9UTT3FJue99D/jmN4GPf1yV12H4kNxtUTlHF8nlfl3PDA2oOquybYAiuXqGYh2vfrV6LxXLapV+VxNJ5t9m+3Z6lYu0nZ3Az3+utn36aZqvm+ZVnLE6Cy65BPjFL+icnE/DRnJ1csvt5ffSqgy0NiY3i125Xqfr00Ui6QJrtl25FSSXF6N0klup0He2Ra55DamAtsKuXJ+MMgO3IO10WFcxr5mVXHnjpySeAoATng9s+O/0vpxByQVIzZVKblaSm7ArW2JyqzIm15Bdeeaaom1kTO6zz1LHxp0hgweYPHZlxoknmpMMAfQbnn8+cPbZ9v1t+/GqUppdea5xxRU08Hzwg3PdkuZBKu/NVnIB4OGHgS99SZ0nCFQsic3u1N+ftCUzzjkH+Kd/iheR/+UvgX//9/R2m+zKaSTXp05upaJ+u7xKbk9PvpjcsbF4KYrjFAXJLdA2GB42T8ybkXiKF81c+7uUHJNdmeNGAdUXMkl56il61Uku9/fDw2pu4SohxKSIky2tXk3H5r5NjgOVChHdn/2MxpP//b+TvwP39TyO8AK1JHxPPUVt0EmuHJM47wQTQinycJ3VU0SuF4ASJa5fHz+mCZzIktvLbduxg/p1/bgAEecgUIsLcvzSx29uu4nkHjiQneS+9KV0j953X9IGLtHVpf6eulrKc4U0kpumlnLOC8B8P+XNrqzXe2bIs38f8AAAIABJREFUBfJmJp4Cks8g79/RQddhSgDnIrm1mmq/TnL5uW9PJbfFJYSYWFUz1IX1Adtwm6HkOrMrGybwM0pqBpJbHSaSG1SAjoyq5Yxd+Yh2fm6PtCu7sitbYnLDaTXoyHgHIN5x5yW5aQmf7roL+LM/c29jgg/JzdohtwLLl9MAlJXIz2c0YlfmFXpXSaW1a4H3vCf+GWdfPHAguQoOxDM++iQZO/98tbrvgsmunFXJNcXkmkhulsRTrOTmickdG6OJl49dewGjILkF2gZjY6rfbcSubHLV8aKZa39pV3bF5HKfxUQRiCu5gLIU63ZlJnjHjvkpuZzASR7/ySeVa0ovG8jjwoYN5rkB9+VHj9J55aIoo16n+dLISJyQnnUW1Vtft05dO59DxrUyidTJ6B/+oYpddUFm/i+V1MIAq7EmktvZSTG/Osnt6lJjEP/OXK2Ar/mBB4APfIDe5yG5F19Mrz/9abIygt5GJrl8j/7kJzQ/ZLu1TnL12NW0uFdpKW5mdmUmuT4xuc1Scm0xuba4WR8ll9uvW5fbmuS2Wsll4tx0khv98fNmV061K+uEUCArye0YiJTc/aTi+irAjDS7Mtunw5og5Sa7smbBlnVyOUOtTnKl9cVEcl0xuQxXfVpGnhqyPiR3IdWmnU/gyVZHh38SCqnkdnVlfw6Y5I6OmmO4Jck1keC8yEty02JybUquj11Zj8mNJZibVgOqjeTyQH3M0PcdRyhIboG2weSkIlWmxFPd3ekkNwzN6hAvmrkmyFmVXCYFgOoLV6+mPs+m5PL1DQ8nSa6uvIUhnUMuuJ50Eh1blhaS4HHhtNOSvwGfo1ZL1rHV41O3bIm3l7f51KfiJJQJFavaPBdatSqp2HZ1+Y1belLExYvJgffjH9NnJpILkKOO7b8mkrtxI/2NdSX3vPOAF72I3h886J7rmbBsGf3ekuTa7MpHNZ5w6qmkBLOtPItdmdv/d39H9XuB+KKKi+QGQTa7Mi8Wu2JyJYHm+1ImYTMpuaaQIh+7sv49P/e+JFfvX3SSa5pXbNiQFCbmBVodk8vJjqZaRHKZ1DViV66NJL+va4RQIkESUyDtylmTTgHKrsy/YaCROmlfZtLvo+QGmpLb1aUy/DFcJDct8RTDh+RmrZMLqAfOFZOblUgV8ANPLHzjcYF4PEeW/Rjd3dThmspZAdmVXF/kicnVE3fYSK4ckKan6bhZlNyeHpXpmaGvDpvAz60+qTjOUJDcArOK8XFy9HAiniyYnFR9nJxkSsKSRnIBswrDi2amsjH8faUSj/+U4D5HOlYkyeX9SiUiUzYl18eu/NrXAjfeCLzhDcA3vhGfe2zaRHXYmRzYlNw0kqvXsdVJ7v3306vJWiz347a//e30ysn+Tj3VfH5f8G/G85sLL6Txo6fHHtO7fr1Scpm4lEqK5C5ZQq4zk12Zz5dHyQUoLvfnP1e5WWx2Zd1lwIsjTNyz2JX5u9/8BvjkJ+n92Jg7W7dcnM6SeMqm5Eo1WLZt5UoiupyEjNuhl5F64QuBRx6JHzON5LrIe167Mt8jLiV3+3bgy19Ofj5rOLwF2HtH8nOpaB6PSm65V5G/2PdTRAJLhgxzme3KA5FdeX/2eFwgGZNb1oi3JOKJtolOw5pdOSK5GzYkVblFi9SNndeu7GMJzUNGfZTcAq1BIyQ3636Mri7qmCcmzBZpueKfFteUBXlicnUiborJLZfjSq7MeJlFyQXiA6+tFIAED8qFklugwOxh926qc/rQQ9n3tZHcLHZlub0EL5pxrgvbBJq/97Erc/wlEJ9XsNoK2JVcl10ZAL74ReCee+j9ddfFjx2GwIMP0v/zKLn1epLkSsI3NORPcrl//uAH6Tfi8zZKch94gOzNfK4LL6TXzZvtzqr16ylsihdTdeJywglEcnW7srwOXoTNipe+lH7Tn/yEzisXpBkm1x234dJLibxLlVx+b1JyFy2iJF0bN5KKzOE53H6es8qxU+bS4Hthelq5BvTtGVmUXE6ieeaZdpJ72WXAD34QT9ImtwPsMbkmIsr7pCm5zYjJnbMauo/8JXDve5Ofz0Z2ZaB1JLdRJbfSZyb301PmeFwgWwkhAOgQSm4ekssxudXIguNSchNJpRzZlaHZlXWrMkDXyPETeUluq0hoQXLnDrzi7huPCzSH5PoouSec0FwF32VX7uoyk1zOkszwicmVySt8lFxJcqWFypTFWUdhVwZQkNwCswwmbnnqTHJd7nI5neSaJsAMl5IL0DFM+8ustDa7cleXaptJyQWUbZYz0tqUXJ3kymOsXk1913XXAe96V/zYgKr3qpNcVnJ1ssTo6KA26XZlvvaXv5ziQ012Zf0agPi1BYGqXmGzFPvi1FMpj4lUctOOu369ynAsx1Aeg5jk7tpF/5e/txzL8pJcgDJumyojyHZIcBsuv5xq/Oq/d1riqVtuAT78Ydr32WfjzgFpjWfIXBryXhwbU/e8ya7sisk1tQ2gRGNbt6o5urQFAyoJ7IjmNPWNyTWR3HI5u105K8nl+2c28c1vAk8/22UhczVFiFqVXRmYv0pupc+s5NYnzVZlIH/iqYn9QFcOu3Kpi841lVJCyNg2D7tytUr2IT2zMsNFchlZ4zSaAR+7coHWII+SGwRqAGmlXbmZVmXALyZXxsECSZLrE5ObheTygMbnsJHcQsl1oiC5BWYVPGHNk7hlclIRWVNMrium1lfJBcxKLduVbd9zP9XbS5P2qSlzTC5AIVGHD6vkUL5KrrS4Pvoo9XN61QYuFfjYY/Sq25V5LmNTchcvpnbpSm5HB6nDP/whkUWOz/RVchlstz3jDPP58+K882gsOOss+za8ALB9u1owAZIkd8eO+PZA/DrykNwTT1TXbLIqA24l1waXksuQia+kXdlGcvle5b/j0aPqXl6xIpuSK59VGZML0O9x5Iiyh+v3HN/3MjmkjKnPYlf2UXJrtfTEU7ZFNDlPyeNSaRSf+xyw9dFuYNpQxiesAeXowlqp5DY7JndaU3IbJbm6xD495SC50R/aOyZ3AJg6DEwdArotpXRcCAKyLE9Fg0JZW/EyxeQyyeXreu4mYM+t0faC5IYA/uAz9IC96lXm89tIrlyNy0NaGkWh5M4duDPOouQC6m/VaiW3mbDZkQFFcoFkqQIXyeV0/pLk8uDpWye3GXblIia3QIHm4uhRez1al5JbrQKPP24/Lis9Oslk8hsE+WNydbXGNoEGkiT73nsVqeRxYXg4nlRHkg7O+8Fxub4xuStXUr/1mteQ5RtQpJbB/9+2jX4PnYS++93AP/+znWgtW0ZK57FjccIBAOeeS21hNRZwk9zu7qR1+CUvIXXxssvM58+Lvj5Sl01lkRjr19Prtm1uJZfxgheo942SXECVUOQkUDpMJDctqaWeXVlXSwEi/oODVDpK2pXTSC67AI4cUSR3zRo6l/582ZRcnrPwsQH1LDDpZ8vykSPmeG5JcuVCepbEU622K8v+jp/N2cTBg0C5w6bkVqNas0F7xuQ2w64MxGOTASK5OplkZM6u3K+sxt0r3NvaUO4BxiMbQKe2OhkYlNwZAh6R3Ac/Avzmr+LbLLkIOO1u4Ad3An/yJ8Ab32g+N8ex6J3QfFFyTSR382a7Ml2gcfBkJCtZ5b9VVnLM+4yPp5PcZmZWBvyUXCBOLsfH02NyG7UrcwkhIJtdeXpaDUpZldx6HfjEJ1T20jZHQXILNB1DQ8ArX2n+zkVyv/UtitOzPZOcAKajw0xygcYST0kl12VX1kn2C18IXHstvedJ+s6d8Um0ieQ+8QS9SmLQ10fkVNqVJanq6SEiy9/pJHdggPrdsTH6O+gkaf36uL1Zx9Kl1Hfv22fO4A+kk1zez0QGg4DiRFuREPOUU9wE9JRT6PqvuYYWJvieMZHcNWtoUYGh/w3y4H/8D3qV5FnCZVe2wUfJLZdVQrIsSq6J5HJCKF3N5cViPdZYklyTXRlQpP/o0bi93kRyXWEHepmvuSK5c6HkHjwIVLq6zEptWCMlsGz5vlG0Oia3GXZlIGlZdiq5fM5p8/c6KqIjzE1ye4GJKDNdp6ZUmWJyZ5TcqI0TIhU5X1epDLzgIopf+fjH7edmJVcffPlh5WQUsw3uFE125W3bVHKLAs1HpUKd8Gwqud3dakCab3ZlIElyfWJyOXZIktw8iadMJDcIzJNpOUBnJblbt9KC2M03Z9tvnqIguQWaCn72uGapDpdded8++lyPwWNIu3IjJNdmV3bF5Eq7ssmCyWCSqyvSul0ZUEquJLlBQP0425X7+pJEVRJb3a4cBOp7n4SXOniuU6sllVyGr5Krq3pzjZ4e4M476dqefFKNoXriKSBJRJuh5Pb0UL3gH/7Q/H0z7MomJRegv8mBA/EMwvz38SG5bE/nv/2+ffFx9NFH6ffUM1t3d6u5gk7AeTGeCbKu5HI7ZX8g5xD6c6yX+cpqV84Sk6v3L7ItnEF7tlCv02/X0dUN1CcMttyI5JY62zMmt1El12bVnnbE5HLiJlMsrwkVsbqTl+RWRMfS5SC5MyWEhJI7XaWkVzPba9d12mnulUXu+GWMDaA6g54e+/5veIP9uI2isCvPLQYH8yu5ee3KPCDMtl05K8mdmPCLyQXUai8PmllLCAFmu3JPj1nJlYQ4K8nl89jsmG2GguQWaCo4M60NLiWXnylbvK4rJpf7w2YpubY6ufr3+oSWVcxt2+Kfu5RcnTQNDiq7sokoSpKrK7nyszwkV9qY85Jcl5I719iwgWzXgNuurJPcRhNPMU480T4+tyomF6C/Jce+cvsrFbquPEruK14BfOhDar9HHqFkZvp5XUpupUL/JibomRwfj9+zpRK1NYuS24hdWS4A6ItiaUquPJZ+3FbjyBHitZ09TAZ1W24VCCpEFn1JWxbMxOQ22d7GJLcjmtjWDRnPXGhEyWV7sO/v1dEEkssZlkudpOpKuBJPhSEwsU/bPqPq+va30wP35jdrx4keVtuKZRjaV+2agYLkzi0WLZr9mNy5ILmmEkIy0YSPkutDcnmwlSTXlh3ZR8nt6UlXcrPG5PIAtkBIbpGyrkBTsX07vdqSIbpIbtqzxSTXFpML0JhcKplJqtxeh09MrknJ5SRFDCaGetkTqawtW0b/Nym5ABHHY8eo/zIRKiZipZK5r2eSqyed8sFSUf3Cx65smvvwbzAfSS4AnHMOxafy31uS3N5e+l1f/vL4PnK8bdV1meYSaTG5WZRcJrl6ojNJcmUJIZl4isdJJrk7dsTv8a1bgZe9zHxNekyubFt3N43HfHx9YaWvLzvJNdmV+XexkVxOaMWCVSN25dkmubwA0dUTXdj0RLzOalgDSpXW2JXDaaWYtkrJ7Y06nLGMaauZ5M6QZO3a6x4lhLyV3CbYlVnJ7TSURwkMduVAZFeWVmUgO8ndvNmsKPE5TDXPZgMFyZ1bfOhD2eNfGyW5Pnbl2YjJ3b+frmVw0J/kmmJyATUQsr1+wwZzMisJn5jcnh6VwVSiUHJnUJDcAk0Fk1yTwgio/sukpvLk0EfJlX2JXjdc/x5oPCbXpvTaSC5/XirFy7IA9H7FCnPiKYAm9jJ0Qwf/tsuWmZW+Viu5q1bReXt6zCRsvpPcs8+m1z3RvJDvncWLKWZ3//7k4kGpRNc7Pt6669KV3CBIj122Kbkmksv3tGy/TnLlvcp/xyNHaBzt748vfDD5PHaM7ndTxmxX4imAftOJCUVy9XvWRXLTSgjZ7Mr8TJls0EySs5Jc7ruCYO5IbndfFzAOInOSE4Q1peQ2264sSWCzsyvPKLlDQMcgMLYz4/66XVlXcifjiwESee3K5d64qpsFrN7qVmVAsytrNXDDEBjXSK4toVZWpCm5rUZRQmhu8T//Z/Z9Go3J5XAL0/4bNwJ/+ZfAVVdlP7YLNpK7bFmU+VwjuWGYtCvbYnIBNRA++igpCUuWKDKZFpNrsivz4Nbd3Xy7cqHkFiiQxOgoqTyc7FCPFWU0y65sU3IBO8nlz9OyK8tJ7oEDNDm31cm1kdyd0Xxs+XIiUzrpWLUKeOABeq/PH5hM8XsdTGJtCwmtVnLLZWq/rW/m/eZbTC7jnHPolTNiSyVXvuro7W0tydWV3DSrMpAce112ZYZLyZV25Y4O2pbtykuWxK+dn+VHHqHX5z0v2T4Zk2tSmVnJ5USOjSi5PKdIsyuXy/Q33r/f/L2+yMUJ7wB7CSHuu4aGzCWWWgkmub39EclNxJ5GMbkIm6/kStLcKiU3KAO9a4HxjCR3xq4c3bSJ38WVeCojye2IlNy8Ki4QV3J1BAEtVIQ1g5IbNq7k2sAPa6HkFvCFbpHKArmPieQGAfDRj+ZrlwummNx9+9Sqvz7Q8qCW1a78zDOqfmPWEkJ57MqlUn6Sm6fO5zxEEZNboCl47jmaqD74IP3fNkH3sSubnq0wVAtbpphcH5LLfYVpgcqm1F59NfDOd9rr5LqUXGkn1n8PjssFknGtTHJ1NwyDSaxtIaERJXdoKKnkmbBunTkeV+43X5XcM8+kVx5b+vpoPE0j5Xw9raqkoSu5PiQ3i12Zkabkyn2HhuIkV147k08uAZRXyZV2Zf2e7e+3J57KYleWJBagZ0SW+OLnmZXgvEru0NDsK7mcFKx3IJoU1rUGzMTkdjY/Jlcer2WJp8pAz9rsSq5XTG6z7MoRCWyE5HJMronkAkrNTcTkTquszDPbNpnkzrWSW5Dc9kGjdmXGbNZlNsXk7t+vJlk6yWUSqZcQcpHcqSlaWT/1VHVOwK3kysRTJpLb3U0Dq54Blbddvvy4j8ktSG6BpkCf2KXVyc1qV+bP0mJyATvJ5cXoLHVyn35axTJmUXIPH6YJr4wVluBzveIVKs6R0dtLfZSN5C5dGs+irKMRkhsESs11kdw3vtGeVLO7Ox5KMt/Q10cJoD72Mfr/NdcAN96Ybg3mv8Vs2ZXT4nGBbImnGCaSOzJCpZ2+/W0zyT10iBZs5P0oldyeHnPJSp+YXGlXzqvk8iKYj10ZoLFfJo3TSa7ev8gFMP3cvA23f67syv2Los5GtyTPhl2584TWK7ljO9zbJ/bX6uTq1+6l5BpWY02YIbmWlUcfuOzKgCK5puzKul058Fgh88F8UXILu3L7oFG7sul9q1Eu0wAlM9Pv25dOcrPE5O7ZQ8dkJZefrTQll1duv/994J574u3g8+sEnUnuypXHfUxuQXILNAVjWuJL20TPx65serb4s7x25VpNLUb7KLk8id2/XyldPjG5suTP0JDaRycdGzbQ69/9XZJcpSm5lQrV5r3gguR3QGN2ZUCRXJtdGQA+8hHgs581fxcERIhc+881nnkG+PSn6f3atUTw0sDkcD7ZlfMouSa78tVXA7feSoRS7rtoERFQk11ZKrmnn24m5Wl2Zb7X2a6cNyaXj+2TXRmguYtUcl0xuTJ7u62E0FwquQcPRjHj/SLxlEQr6+Ty8TqHiBD61pX1QcKuvMefdAJCyY0Imq7K1h0lhJa9lF4Xn+N3rlbblQG7kst25bIgBc0qRF4kniqQFe2o5PLAIBXRrHZlV0xuZyfw8MP0npXcIKCOO03JBYA//VNK4vI3fxNvh43kMlHNQ3IXmJI7J8tjQRA8A2AYQB1ALQzD84MgOAHAdwFsAPAMgDeHYXg42v46AFdH238gDMOFUaV4AUEnuWlKbla7sk5y5fGbreTyJDcMKSaXyaJPduVyWdW5HRpSx9QJyyc/CbznPUQOdHDsZxDYCdXdd5s/B0hRK5UoiVIecL/uUnLT8J3vkFq6kNBqktsMu3IeJXf79nhdZ13J3bvXbFceGaFnZOvWZDZqRlcXPXvSUSXb1iwll5/3SsVcRsxkVzYpuaaYXNm/+Cq5Ydg8npGGgwdpUalUtii501F25aAM1EaTB2gE9eiH6BhU5640yc+vk1yEwPhuoM+zY2tEyV13JXDlLqB3td+5ZsOuzBmW9ZjccJoWAAZPBw4/mP/8JhR25QJZ0Y4kV5bzKZdpQjs6ml3JrdVU58/HAui6ePLLJJfPm6bkAsC11wI/+IFaDZZ2ZSA54eUJ+YoVtIqdZUAqYnKbhpeHYXhuGIbnR/+/FsBtYRhuBnBb9H8EQfA8AG8BcAaAywF8MQia5cUp0CzwM/WOd1ApkWbblXWSa1NaADvJ5XFaP760OgJqknvkCPUlLiX3wIH4sUolFasqSa6ucvX1mQkuQP2my66chvXrKfTj9a/Pvi9ASm5HR2NjzKWXmu2r7Yx2ILl5lFz9eZDnHRoiq/Lhw6rEEqNep/t/505z0ilAzVmmpug5DQJzTO6RI/Sdrv77xuRKJdal5PK5ly+nc/I2tpjcMIyTXJ7ruGJyOVHdbIEXIGZsrIkES9UW2pWj4zHJ1VXkRpAguVBxuYceBA7cS+/rE8Ce2wxt84jJdWUh9iW4AF3/mX8MnPgW/310ZLUrQ9iVJ/YAA5vzn9uGZtuVf/1r4Pbb/bcvSG77oV3tyoAaQDkroU3JtZFceQzdrgzQbyMnRnoheEYYqhJCDLZVyXbw+W0kd+lSlQnaFwtMyZ1PduUrAHw1ev9VAFeKz78ThuFkGIZPA3gCwIVz0L4CDvAz9ZGPUPbaRuzKaSTXJybXlJTGZlfm/kLG5FarisDytciY3GqV+jL9WOWymeT6EBZGml3ZByef7BfTacKmTWThnS0lql0w23Zln79fnuzKupLL4OdDV3K3b6fj6kouANwb8QxT0ilAXdPkJLkbBgaSdmVWcvXvuE1ZlFzfxFO8QM9zGVtMbq1G187zrSAwlxiT2ZUBc/+3bZt90b4RHDrEJNeSeIpjcltiV45+iIpQcpuFGMnlWrmRdeaBDwP3f5DeP/Nt4PZXAWPPaW3TSa5O/h125awIAuDsPwWGLA+CD7LalaWSO3UY6Gpy7VB5jmYpuWeeabd9mFDE5LYfuANuNLtynv3zwkZysyq5chsTyT3ppCRxNdmJuR1y5Vtu62tX5tgz3WrpQkFym4IQwC1BENwfBMF7o89WhGG4GwCiV87gsAaANIXujD4rMI/AE1HOVNtsu7JMPJUWk6uTYD6fTcnlbfXEU7LECBAPr5iaUv2GJIOlklr0ljG5WQgn25XHxlqXydeFj38c+NnPZv+88x0LNbsyg9VYneQycc5DcpkcTkzQ+KwrtbKEkClRGpNczgdii8nVSar8TO4n7cqAsizbSgjJxTWGaRFNKrlAsozQ/v30G333u8lrbBQzSm7JYlfmmNxWZFeua0quTrAbgSS5PVE6+vEoC+DYDpXoijMLTx3S9k+pk1t32JXnAmxXtpHVkmZXljG5tTFF5psJfgCKmNwCvmhHu7KuwnLChjQlV7cQym1MJJeTTjGkOivBA0yjSu6SJfH/+6BIPNUUvCQMw+cDeC2A9wdBcIljW5OeFBo+QxAE7w2C4L4gCO7brzOUAi0FP0O9vfQ8c1yaDh+78mwnntL7E57E6reQruTyNcvJuc2unFXJBeaO5Pb1AaszOPWOF7Q6u3IzEk/ZlFwml0EQH5f5Xl2+XI3nOsllLFlCx+WEjwDwy1/S78KJ1HRIJXd42F4u6+hRcwx4Xx9dGz+jUgm1Kbm+iacANZfRlWAXyXUpuXwNupK7bx+1QcY+Nwuc+dqq5E5XKSa3ldmVW01yK9GNU4tWPMZ3ATVeMeU4tZH4/ja78tRh4McX034uu/JsI03JDXQlN3pQp+tAfUzZnZsJHuSKmNwCvmhnuzIPFK1ScmU8LkADs4nk6sqLvq0ek2vKrtzVpRanCiV3dhGG4XPR6z4A14Psx3uDIFgFANEr577cCWCd2H0tAM2XNHPcL4VheH4Yhucv4xlbgVmBTnKBJNEMw8btyqY6uRMTfomnOjupz/FVcvV4Wz3xFF+zzGKsK7m2mFwXJImaC5JbwIzZUnKZkDZTye3vp896e+POAyadGzaY7cqyG+VEYps2ARdGASOPPko1k233t25XNim5ExN2JZefJXaKpMXkysRT8nv+XXSSy0quLSaXx3s538pDctlltmtX8hobxbFj0XltMbmtsCuP7ybCnIjJbZFdudRBpK4+RiS1PqFI7dRheq0Oa/tbEk/tuRXY/3N6P5+U3MXnAYvOBPpPMn+fiMmNHuR6NBC1QsnlQW6ulNzBwSh1eDEQtg3aUcnVB1KbknvsGK0ku0guDyajo2rS4FJyTXZlHmB0u/LYGB0/TckdH6dz8/nzkNwi8VQ+BEHQFwTBAL8HcBmAhwHcCOCd0WbvBHBD9P5GAG8JgqArCIKNADYDuHd2W10gDZLkct9kUkz52Wwku7JPTO6WLcAXv6g+k3W19eddV3L5+DYll783kdxmxeSa3heYW6xaReQob6xzGvheYbLXSJ1cfV9O6qQLMnyvbtxoJrlvfjPwr/8KPPGEsiRv2QJ84hP0fseOOBHWkWZX9lFyAVoc27lTJZcE4v0EP4s9PdT+ctnPrsxzmSx2ZRPJ5b7LRnI5ed1zxuXZ/AhDFes8Y2NNxJ422a48cQC4fjXw4P9urZI7LUhuEJBSWRsnFRcQJDejkrvvp2qb+ZTD8oTnA6//NdBpSWvPhFwvIcQZs8u9wGW/AC79UfPaNNdK7tveBtxxR76i7wXmBs0iuXMZk7tzJ7WfB0geOK68Evjc59KVXC7NwYOjTcm12ZXlqqvcFqAO38eu3CjJXSBK7lxE868AcH1AckIFwLfCMLwpCIJfAvheEARXA3gWwJsAIAzDrUEQfA/AIwBqAN4fhmEL0ncUaATsjiiX1fM8MRG3Jw6LhfZGsyvzM82ZTHWSe/Ag8P73A7/7uzTpZZLLdUElfGNydTuzj105b0wuoyC58we///vAO9+Zvl1e8D3MY5nPwojukOIx2rTvokXJZGJSyWWXhbxXe3uB3/7tZDuZrNZqbpKr25V1G7xUck1xvTy3Hh0FXve6yJYLdUyGngxTJ6I6ye2KSY+UAAAgAElEQVTvp3PblFz+v43k6nOKyUn6nJ9Xm5LbbJI7NkZ94OAg0hNPNcuu/Hi0enjk16qebKuVXIDsvPUxYCwiudNViqtlJTeV5EZt2y8SDhz8ZfPa22roMblsV2aSW+kFljY5J+dcK7l9fcBLXzo35y6QD80guZXK7CYb0wfSO+4AXvQiNWDKtmzdCpx7Lr3X6+TyMZiIcuInl105i5ILECn2sSvnJblFTG5jCMPwqTAMz4n+nRGG4Z9Fnx8Mw/CVYRhujl4PiX3+LAzDk8IwPDUMwyYuUxZoFqQzw6bkylIgjdbJdU1C5eIXP9vVKvVTJpJrismtVpXKw5CJp6pVZaHUSa7JrpxXyW1V/GeB7OjtpazTrYKu5PrcM0xI05RcgMZI/X4yKbk+55XCji/JtSm509NEUl1K7ugoqcZMEnUSy88q25D1kAa9hFAQEGE+zE5XS0yur115YoK2kcq1RKuUXD7u4CDsiac4Jrfc1biSO10Ftn2e3vesnr2YXIASM9XGgHHxI9ZGlJJrsiszuQeordVjwJFfAad+iD476ermtbfVsNmVmdwvxJjcAu2HRrIrcwc6m1ZlIK7k7tkDPPQQcNll6ntJcvfsSVdyOdaNSe5llwFXXx1fpQWap+TqAxKX5uABf3SUYotsuPlm4E1vove6kvvmNwMf+5h933mO+VRCqEAbgxeOgPjEVoJJbl+fOybXJ/GUy04oS47wJNCl5OqLZvy6e3d8O50E83l87MpZlNzCrnx8YvFiqnHMi8S+CyOynrwt8RRA96M+Vz39dBrbLr9cfcfHcEEKO42QXJ7LjIy4Y3L37aNr5D6ktzc+rpuUXNnH8LMqSb4sT9SoXZlDJmwklxfr9+0z932+2LKFYqD5evm4fkpuJ70PPf7ANkzsAya57tKxZExuq0oIAUTi6uNKyQWA2rBbyS1VFDmsTwKH7qfrX3U58NYQWH9V89rbaiQST2l25UoLV0TnSskt0H5ohpI7lyT31lvp/atfnfweIPuPKbuyjMk1kdwvfzl53kWLaPCwZUO1Kbk8iKyKss5LRebrXwf+67/iSu4tt9Bg/6tfJdsAUO3qf/1XGgil2hSGtK8+aW4jFCS3QFNgIrn6RI8nqENDyUmirFedVkJITmB5EUzaordsSZ6zVku3K8uYWyCpuujf8wTTJ/FUFiW3sCsfn+juppq0V0YVwn0XRiTJtSWeAqiG9bXXxj/r7QW+9z1KKsUkVy9/Y4KvkstzgPFxe3ZlhkvJ5WdRliobHqZYYUCN8Tyn0IkoP/N6XWDp9ACSiaeylBDq7lbXo/+G3FeEobJI58EvfkHhYs88Ez8uKbkVsrBaY3ItSm8W1MQKYvVoUsmdbqGSy3blcUFyqyMqu7Ku5E5XiRgGAV3/9BQwGU0+e9swfbzVrhwNcq0kuYWSW8AX7UhyZQmhO+4gxfW885LfA4rklkpxpdWl5NrAq766Zdml5Eq78saN9Prss2q7P/xDel6vvVZNJh97jF63bo2fJwzjGWEnJ+NK7q5ddL4zz3RfxzxGUWG7QGZUq6T2yImfJLk2u7JUPnWVVE4as9iVt2+nV878CsT7i+Fh6rfCUJFc3kc/n67k7tlDbWVLo8yuDKgkOFKBCgJaXCuXyTpZJJ4qkBX8XOVRck0uKsYb3uA+TitILl/L4cMidlRAzmVsdXIBlZWYy5L19pKj7IwzyMa8fz/NS/QM6AwmuVKQ6u1VfVJaTK5PdmWXkisX1nbtym97Z7LP5DxGcgEiQAmSW1VKLhAR05wdi1RLq0dnr04uQHblLEpuWCPiD6h45KloVbSjDRMZzdiV9cRTbFduIREtlNwCvmhGCaG5UnKZoK5dG18pNtmVe3riSS5kTK4vyZXEVQ6kvjG5a9dS23bsUOd+7jkiuldcQclpuM2AWh1lXH01DYJSmZIk9+GH6X0bk9xCyS2QGb//+8BrXhP/bGxMTUhtSq7MRmxK3MLwKSEUhjSx52dW1un8gz9Q74eH4xNYHyVXkljOwio/535HJ7ncJ77pTcCDD9K+ReKpAlmRdWFEklxekM0zJ81CcjnJHOBHctlea4rJZZiUXH629IUpfkampoAbbyQll+NxgaRdeXiYziXnKia7sq2EkE92ZVdMrlx4ayQu10ZyZxTqcrfZrswxuUBjyaFYye1ZHdmV9ZjcFtuVOSa3Et3gE3vVdjWDkjujfkaZpVn1tWUwns/wSTzVKhRKbgFftKOSK+3KUrFhyIFjcpLUXH1ylkfJlcRVQk8UA6jBU5Lcri7K5sgkd88eWk3mVVS+DlaVnn46fp6tW4Hf/Cau5MrEU6z8mrJCtgkKklsgMx54QLkfGKaSYDYld9EiewkOwE/J5e2Y5K5fr7b9zGeAe6MiUyMjarLLiad0Z4hNyQ3D+MTZRIKBZKKgjg7grLPixyqU3AK+yHrPVCpqzBsepv3lArAvspDcIFDbN0Jy05TcJUvo9ckn45/LZJL/9m90fNmOUgn4+c9V/2CySkuSK/uItJj/vEoun7+ZJDeWeAowZ1AOa2TbbaZduWcNqaI+Su5dVwIP/b/Zz2WzK08dAvo20GejO9T2VYOSy3GsXCN46iiRw0obKpN6TK6phFCrUJDcAr5oJPHUfCC5XGPWhaeftpNcjsnlCacLaXblNCW3UqHJL5Ncfl23jl75d2Q7ok5yjxyhc/NAIpXcqSlScleuVANxG6IguQUy49ln6Zlh6yCQza48NJRUcuWkMC3xFD/31SopPKtWJftTVrJ0JXdw0D8mF4hPnGV2ZUAtvnFMrkmtLRJPFciKrPeMruTmdRbyPFYnaDbweXxicpnkZo3J7e6m8+gklxeYL7iA8oQ8+WR8Qaqvj/qpK66g/9tIrm9Mrm5XNvVfaUrupk20OMDOsTzgBXmrXbnclSSa05GSG7Mr58QMyV0F1KLEU5I0mqzSz/0nsPMGZAaT3JLMrjxONuneNfTZmCC5RiWX7cpCya0MKhW0nVDqoHbzNel25VYqubNZzqVAe4MHsHayK8uYXJOSy4os1xB85hk7yb3oIkoytXRpsmafjjQlV05Gu7ron8yuXKkQoWVyu3MnvbKSGwTxa9HtykxyWcnV7cpbt7a1VRkoSG4BA4aHgZ/+1PxdtUpqgnQ1AH6Jp2RMbr0ez+Lqa1c2KbnSqszgCe3wcNyKODBAx5KTVJuSC8Qn8CYlt7NTXbeL5BaJpwr4olG7cqMkVy+5l7Z9s+zKJiUXoEVkjsllMKH95CfpWd65M96OL32JEng9/DD1Q75Kbl67so+SOzRkXmTLgtSY3HJ3nGhO1wGEFJPbbLtyOE2qaqlTqMT6hT9OZPPo1uwKslHJHSWbdE+UOGosmtQhMGdXDoTFd3qKlNzONozHBeK/MzC7duUCBXzxlrcAf//36SqmCXOt5NZqZpLb30+qDsfC7d6dnJxJa/GBA+lWZcBOcmV8nr69VHLLZUVyp6cVyWUlF4hfy7PPqslCGCaVXD3x1GOPAaedln4d8xgFyS2QwL/8C3DppeYa1bt2KQWXHRCAXwkhva6sJJppduWpKVqUqlSANdEi/jPP+JFcPSaXP2fYYnKBuDpkUnJ7e93KW56Y3KJO7vGNRkiuicz5Iqsjsb+fzuVypWWxK5uUXMA8V/jxj4H77gMuuUT9XvJZfcELgLe+lcb9Rx4x/y4y8ZReQogX4XztyhMTaqGd/y9x7Bidf3DQ3K/6QNbuliS3s1O0T088FUYXVqo0x65cFyQXACb2R+SrA0CQJNBHHlbtOPpwtnOZYnInD9LnMyQ3UjC6V5jr5Eoltz5JSm5HG8bjAlGGbDnpnUW7coECvtiwAbjmmnz7Vio00ZtvMbkMmaRFb6PudvAhuTa7Mqfgl4MaQIPkzp1UIqhSoYnlunVqcNixg9ouV4zltVSrasV4YoIGsnpdDdATE0q9qlapXa5V7DZAQXLnAFu2AF/4QtzuO59w4ABN8DjmVEJmKtdJLk+SbWrG2Bg9kzzZzEJyWSkJAuCcc+izBx+k9phILrdFxuTaSG4jSq4kuSZSkkfJ7exUpLhQco8/zLWSm2X7tPEvi13ZpuSa5gpr1xKR7ekBLryQPtPbcvbZ9PrQQ4pk6u0fHaV+WPYR/HxXq/4lhCYn6VpLJdpWj2seHqb5TCMkd+9eNWZIkhu7Lj3xFJPcoMNuV56uAY/8dZIkmsCEikvwTO4n8hwEZqv0kV+r94ceSD++RD36EZm8lXtU27tX0isruX3rLXVyZUzuFFmd21XJ3fBW4Mw/Vv8PhF05KGkEuEmQZVQKFGg1mODmiedtBLpd2TbxkrGpsnYkkBywfZJb8MruBz5AGZEZO3fSYLJyZXL7//gPitH51KfoM05Is2MH7bduXdwmrVsNn3qKXuUEn1dPR0Zo8i+JsW1gbhMUJHcOcPPNwO/9npqozDcwATRNxnaIEKhDh9R738RTvb3xSSTDZFfW7cx83E2baIL6ox+RAmMiuZUK9VM2JXffPnM8HhAnuabEUzK7cm+valezYnKDQPWxBck9/tBITO5sKrkbNgCnnurehheb05Tczk774r2J5MpF85e9jF71Re+TT6Zj/vrXdrsy1+fW7coA9UO8+OZbQoi3bYWSKxNWycRTsd9UTzw1LZRcm115z4+BLR8F7v9geiOY5DLJnNyvyFWpGxh+ArjrjarMz9GHgcFTST09/GD68WPn4ljTaNVG2nE7FxP5ZSW3d12cpP/sd4CdP4gna5qeBKbaWMld9hLgdFE6gKdvtREqH5QW/5cH995rTpJRoECrIOM+ZgtpdmV9OwD40z+Nf8eD8IteRK+/+U36eaVt8C/+Qr3ftStenoNxxhk0Ab7zTuBjH6PPeAL8+OM0Qdfr0/G1XHAB/a5/8zfKqqyDP5O2qoLkFsgKXgAy3WPzAS6Sa1JyazWa9PmQ3L6++CSSwZPCSoX2O3KEFs1+8AN1LEkmzzoL+Pd/p//bsptzuSA9uzIAvPGNZGeU7UhTcvU6uT525TxKLkDHrVSKfB/HIxrJrjybSu6XvgR8//vp23V1qb7OFpPrGkfTSO7ll9PrSSfFtymXKWfGQw/ZSS5AcxrdrgxQv2AKjXKVEALsJLdRJVfWFpdKbuw31dXU6ajzCxx2Zf7/3p+kN6I2SmpxZzSITexX5LncDey+icjlrZdGE6mHgUVnAYvPAQ7/yucyFaojpMRyXVhpx+1cBHT0RxmUS5TtWSq5z36PXmWd3HZXcnUwqa2OtC4eVz4QBQrMBvr6Zr8usy/JBYDrrwd+8QsijRIXX0wD4u230/+vvTZbG+Q179yp4vIkvvIVyrJ48cXqszPOoAH09tuVkivB17J5M/Dnfw788IfADTcUJLdA68D3jLT7zidkJbnsytCzK5sST/X1mZVc3nZwkCaQW7bQ8/ZgtPg/ORkfa885h/bfvBl48YvN1zEwQJN+PfEUQAtlXAaJ2y9VJYacYJuUXEnaTaSE98lKcnt6ChX3eEW72JV7evz24cWpSiXpQuNnzhaPC6ST3Isvptj8F7wgud1ZZyklVyfY3F+NjpqVXLYrd3bGRbKsSm61Sv9vlpJbLjtIbklPPBW9L3Xa7cqTUU3H0WeiRFUO1EZIWWU1VCq55S5lMR55Ath3BzD6FCm5fRuAsWdNR3SfqyxusLLoEDsGgUrUmQ9sJuJaG6VkWHVxfbJObr3NldwEopuyPlrE4xZYOPj614GPfGR2z8mDLXesLpJ75ZUqRkYiCICrrqIBIAyB667zO/drXkOvcsK3a1dSkQXMSkq5DLzylUSwd+5U9SsZfC2LFpEturMTuPtuMwHhBFgFyS3QCPieaVcld9Mmes92Zb1fsCm5vEAmsyMzeFsmuQ89RP/niZ2cRAIq3u6977W7tPr76Vr4uR0YiKs5HIawZw/1HRxuodff5om4HpPL5dSabVcGCpJ7PIMJ3GwnntKdUc0CPz8DA8lntRlKLgCceKJ5302bKJZ1dNSu5NpILtuVdeecq4QQkCS5spZtozG5QUAL/E4lV5LcqaiT7jrBbldmkgsAR1LU1tooUOkjJZVREkquxJ7biHT2rgV61wPjzyn7tA9qI6TWMqRa2TGobMxDZ0fvQ6A2liwlBCiFu3psASm5IrtykVm5wELBy1+etOW0GjygcGc9mxk/b7qJrMdHjqikCzYl14ZXv5oGg74+4N3vjn8nSW65DGzcSGpwoeQWaBXa2a68axdw+uk02eKFIJ5w8aSRE+Sl2ZVdSu6vo3wlNpJ7xRVkN776avt1sF2Z61KuWhWf6B46RG3YvZvi+5lUSCV3YEBNXvXsykBr7coFyT0+EQTx5GNpaJaS2yrwc8v5MSTyKLlB4P/byMVwF8mt1dRx5SKc3u8A2ZVc7k95kc3Ur46OAv/4j/E8BDr27aOFuMFBF8nVEk8xge1aKuzKeuP3i5PcaW8AoEhuhzjpTExudPygTP8O3EP/710L9K0jwju+G95g1ZhR1kguY+hsoGNA7SNty6PPqjZOHQQQLkAld6JQcgsUaAQ8QePOebbLWixapCw/o6NEDkxKrg2XXUav73lPMiGWJLkALSA88cRxQ3KLiL85QDvblffsIafG0JBqP5fh4GeJE+TltSvv2ZNUcqem4pPNNWuAb37TfR0DA5TwhknuypVJJenAATrH6tXqMxvJNZUY8iW5hZJbIAs6O7MrubWassXOJ/Bzq7uoABV3nkXJzRKn7kNyx8aoL9LDEVwkt16nf+WyKjUkSa5MrClr2XKd3OnpeJ/wf/8v8L73AaecQiEYa9Yk+4x9+yi5Vm9vPPFU7Lr0xFOTB+m1a6mKbWUld/ctwP0foJjZ3vVAWAUOpSSHqo2ShbjSDyJZoVJw+bVrCdmmD95L/+9Zo8oAjT1LhNcHVY3kVkSHWBkkezVAJLca/cjV4bhSzYmpSp3ARGTdWTBKrhjMCiW3QIH84MF2LpRcIF4vlweMLEruxo3AT39qjtnha+HV0JNPBu66y0xAFiDJLZTcOUAz7MoPPOCXvC0PRqKFcJ3kcjmtFSuAE05QduWD0TxKKkhdXXYlN82uPDEBbN1K/7cpuT7o76dr2b2bJsZLliQnuvv20TlWrVKf8SS3u5vaqpNcm5LbzJjcQsk9vpGF5HLiKX5u55uSy4TPRHIBer58lFy+riwkV+bgSLMr20iublfWF+m47CDPSXp64gt8ct7A84wRITYC5B4DgG98gyzWX/lK8lokyT16FPhv/40+S9qVDUpu5xKltDIJPPhL4NhjwIGfA93LgMXnAUe2JE8swUpuUAIQWevWvJ5e+fgdQ0Rk2Tbcu4ayHwPA6A54oz7qVnKrURxKTMkdTpYSAlQJIWABKbli+lYouQUK5IdOcmd78iVJAQ8oWZRcgJJTmNptUnJHRigbsz7J0GNyS6X5N6HIiILkzgEaJbnj47Rg06oSdjYll+vnrlxJjgheCLrpJnpWLrpIbdvVZVZyJSm0Kbk7d9K269bRJI5dHFlJrrQrr1ihalhKJXbfPiLBUsnl73lSzP0G9wcnnaSuQdqvm6nkvu99wIc+lG2fAgsHeZTcZpDc++6jsa+Z4LJjNpL7v/4XETYbmOSuWEGvWRaMXEqunnhKD0ew9TtMjrmf5AR2p5xCr7qLha9/7VpFSPW+lUsX/su/0ILF9dcnr0WS3K1baZvNm1V2aQCkoBrtykuUrZjtyhyvO76blN7F5wFHH0nWupVgkitxyu/RKyu5nYsVqS110LH5/2MZSG5CyeV6ud2kSjOx6zsRGIhqWe34N3O9X1lDdkEquRmzxhUoUEBhLmNygbiSuzOq/Z1FyXXBRHIB4P77qXyInBDzpJ63XbQo++R1nqGwK88BOjpoopTXrvytb9Frq8rX2Uju3r30umJFnOTecANw6aXxUIDubnPiKd2u/K1v0aRVklzGJZeQJXnPHmqLKabPBUlyZU3tgQFqy8QE9Sf795uVXJ4Ud3erOGOAruHFLwbuuIP6jyCga2pmTK5r0l9g4SNPTK6M/cwLk9upWTjzTPPnf/mX7v2WLqV9zzuPFM8sSm5fn+qrXEru3r2qXFhaTC5vd+AAvd+2jf4vSa60KzPJXbcuneRy3pHbb6dj7N1L98H69fR++XJaaOT9//7vKbHmDDoXERGdrhLBnDxIRLHcBUxrSi5bmQGgK1JywzqV/VlyfuK3BBCpq9EPd+GXyIrM/58huUMUhwtEVuUStasykCS5+34KLH0xUDJ0kHriKSa1HI/72i10vCAAFp0GnPgW4NG/BXqiFcvzPw8sv5Tel8QfsWOBkFwUduUCBZqC+WRXvuceGpxs2RSzwkZyH36YEuzUajSYAaqgPa8st7lVGSiU3DnD4sX5ldwvfIFeORtwM1Gvx5OaSMjY1sWLya68bRvZpq+8Mr6ty64s7YB//ufAX/2V2laWJOEJ93PP0W+V9XmTdmVJYgcHgfOjORwnuDLF5PKElEmuxCteoa6J9zER2bxKboHjGytXEqHxQTOV3FYiq/uKUanQc8p1rbPWjubzumJyt29Xcwp+ZicnzXZlJrk8H9i2jeYR3IcsW6YWBAHKSL94Mf1dbCT36afVHOSSS2gB7o47gHe8g/5NTVEfyEqu3pYZdEUfMIGdPEBKKpC0K7OSC0RK7rn0/rDDsiyV3JPfA6x5nfqOjy+V3F6hRvSti5PcQ/cDt17y/7d37tFVVPce/+68Q54QCI9ACAECAtXWohasWLVapa3WXm4FK2qXrGpdvaVrqffial1tvbcWqbZYuLfVJbXVtpZe9PqoWtuC2ioUqRStFEWeAaGFhEd4GCTJvn/8Znf2zJk5SQ4nZ+acfD9rZc2ZOXNm9jm/zOz9nd9jA1sfDDmXv/CUE1JT4PyIleOBYRe675/270DncWD3k7I+/FKg2plE3fbk5kq4smK4MiFpIWqRa4d3PvecDDB7G7oYhl/kjhnjemyqq72eJYpcki6qq1MXuW+9JcvW1sQqn6eKnSsWJnJNTu7Bg5LrDrhTfRnCwpX9Obnbtskg8OhR73Q8gFfkHjzY++utokK8Htu2eT25ixcD994r7djgjOeCRK7tyfVPr2Lm4t6+3f1MOj25pH/z7LPAwoU929cvcuNWeGr2bPHChk311VNM2kBvRa7Jy03mybVFrtnvyJHgcGXT/9sit6nJ/X719RJabLy5u3a5bQgSucePy4O4efOAOXOAhx6S+80f/iCe63Xr3HuvX+QmPAgpMSLXadyJVkvkOjcxE65se3JLhgDljSIqD/0VoXT48mRtjCe3sNoVuaWWyB0wCjiy2T2/qb68/ach5woJV7YrK9uUN8jymHNTLrQMbvJ0KycC5WOCP5910JNLSFqIS7jyunUyqLzssvQfe9AgWRYXu6GC27a5nVJhoRueSZFLTpVURe7x4zJ4amiQddtjkA6OWOlMYeHKw4aJd6SlReaULi115841+MOVT56UP3ue3OZm+S4tLZJj1tDgDijLyoAJTprV7t3Slt5eb8bT3dbmFblXXCEVomtrgdedaSFtT68/JzdI5M6YAdxyC3DPPbJeXBwscs3n6MklvaGmxhvVkIzSUhFq5tqNmyf30UelUN6p4p/Kq6eEeXLNOObAARGRRuQa4bhvX/JwZb/INZjjmDDl5mY31SJI5O7YIcszz5T0jcZGafO2bdKu48dF8Jq22eOvhDmEjSe33YjcFsnHBcTzl1cY7slVCqgYDxzdggTePwj85T9kGZb/mR/kybXc90MvlJzf358ncdkHXpPtLWuAI75zah0+hVBRiCe2sEqqOB/bKesFlsHLHANM/wWQlytZWpbIpSeXkNSJwxRCAPCrX8nSU2jhFJk9G3j6aa8nZ9kyCS+aO9ftlOzQUIpccqrYOa29wVQyNvlt5gl/GPfdF1ypM4xkIvfvf5drv7wc+KAT2fb448DEiYmeyooKt1Ab4J1L13g3jUcaAF5+2VvQafBgGUwWFEg4tNa9v95s77ItYg21tW6F6KDCU8nClfPzReCedpqs05NLoqKxUaa9i6vITRepityxY+Wa9ldwzs8XAWvuQ0ac2iI2KFzZ9P/r18uDua1bvSLXCNpmZ4rWME9uZ6c8uDfjGvtBYX098Oqr7ry5zz0nS1vkVlUFRLQV+z25Vrgy4J1iyCNync9VjEsUnACw5zfApkXyOkzk5lk5uWWjRVSXWV7T024DJn9dphZq3ycit8rpyN59xnusznaZV9c+l5lCKMyTq/KAokFSRVnluZ5lAJh4K/CZXcCgPqrWGAV2uHLxoOjaQUi2E3W4cnm5PGRsbpaCU2PSGG1SXg586lPebVVV0sEtWiSd0oAB7ncuLXWfsFPkklRJ1ZPbW5G7ZElqInfIkGBP7tChci2ays4HDwKTJiUeZ8QItxI64Oau2uHKtsg9dkym7zKisKZGRGNtrVu91D/HdXeMGuV6b2xPrsHc10pLvblt+fnyZ1dX9nty/YTl5JoBcrrSKwjx09QkD5RM8aK4hSuni1RF7s03A6tXB8+uUFbmTsVmxGlJifyG+/YFhysXF8u44KmnJMwZAM47z33fiOWdO+W+duBAsCf3mmskouRb35Jtph6IaYvx8ALBIjchHxdww5XNnLDvt8r0QYa8ImDvb4CtDwEnDgAlTslqI3LLxwFHtwFdHd7jvrfXfR3qybWqK5cMAS5+BRh7g/u+UsDgafL68F+BwxuBkZeLF/J4s/dYJrw4WU5uEEbQF1R44+Pzi7xe5ZzA+n5Vk6NrBiHZTtRTCOXluU9hwyo0ppv8fLlHVlaKEDYdXWWl+zoHRG6uxO1kHamKXFMEzVwHe/eG79vZKQ+GeuNFNNd4XZ2bb2qwqxQPHy6DrP37g0VuXZ3k0nZ1yfXbncgFROR2OGMrEzlhi9xUrrfrrgOWLnXTEWymTJEpU37yk8TfqKrKDVucMaN7kRrmya2sBB57DALA+UgAABPjSURBVDj//N63nZCeYLyIJiSYnlwv5eVuoTk/ZWWJnlxArv2wcGVA7n1mbtsjR7y/eV2d3Auam72VlQH3AcTTT0sF5fnzpWjfiRNe0Wq3parKjfqxRW5gYbKiGgBKPLmd7wMn27ye3PxioG0T8OebAd0BNFwDtP/D9XBWjJftx5slR9fw3h73dbfhys6NevBHEvepGC/L5sekkvOgD0txquO7vft1OB2GXV3ZeGfDPLmAJXJz9CKwsUV8VYYGxoTkIv6c3EyLXEBu9IcOZU7kGq6+Wub3MyFFlZWutykHRC49uRExcKB4X0w4mmHtWrfibxDGk2uEZZAnV2spWvPb30oe7L59PW+XLXLb2twpLQDXkwtI/2pCloNE7siRIlhN3poRufY8udu3y35GHPo9uYCcz3iEU7ne7r0X+OUvg0Xm974nA9XPfS7xvRdekJxbALjxxu694WE5uYDk+PdFJWxCAFfkrlsny57m8mYbqYrcZJjfSilv9WfzAC+ZyAXkoZ//oUJhoUSy7NzphiwbT65p+6pVcr677pLp1/yF++zp0r79bfe1iSyz2+AhL19ycNv3ixcXcHNyAWuuXKcqYPUUYPojrnCtGCfLNt9kybYnt+M9BJJneXLDKG+QvNnm5bJec454WN9717tfkCcXAKZ8A2i4Ovz4RuQW5mg4gwerwykNyMchhPQM25NbXBxNfpnx5E7OcFTGJz8JLFjgdrAVFdKWUaPCJ7fPIihyI6K6WgSkPyT46quBr3wl/HPGkzt8uAinIJG7axdw++3AbbfJ+qFD3irM27eL8NqzJ/GzpkJrXZ20z4hTQMSmnbuaTOSaeaxXrADuvDPYkwtIcSmz77hxbjuN59X2VqQicouKgKuuCq7sOnBgYsEsw+mn9y48OixcmZC+pqFBrql335WK5Ln6f5hqdeVkmPtMUZH7gA1wPbnt7Yk5uYCblztuXPBx6+tF5Jpw5qA5vh99NDz1y+xfWAh86UsSir52rdzHknpyAQk9PrFf8nEBrye33ffEs8gX4mJErr/4lO3JLQlS1/AWngojrxAoa5ACVmVjxItbanlyj2wBXvk8sOZaWfeL3MkLgCHTw49vhyvnOnandqqlywnpz5hOs6Mj8/m4BjPAzbQn12CHKxcVyRPaK6+Mpi1phOHKEWEGV6tWudW89++XwYw9jY8f48mtqREvQlC48urVsty40d3W0iIC9cQJyac9fFj+f+fO9X7WeHKNmG1rE0/F4cMSMmeqOgPA9dfLPSFooGeE6x13yOfMoNAvcidOlIHk3r0ysDPfz+xvPMdA73NyM0lRUfqncyKkJ+TnyzW4aZPMqZqr9IUn9+GHgbvv9t7XABGQr77avSfXzqO1GT1aROn27dJe20u8cqU8pDRF64IwInfECIkQGTPGrUWS1JMLiAg9sV8KPAFAlXWiTp8XttgXYlIyTLy6bW8DG78DtK4Fpj8KtO8F6v8V+MA3gcqQhttTCCWjYhxwdCswxJmHbUCdiGjdBbxyNXBgnbtvb8OO/+nJ7QfhyiYnl5WVCTk17E4lKpFrPLnJOoa+xHSwlUnSQbIQityIuPxy8YRedZVU8772WhlUAeJBOHAgOI+0pUX+BwsLZaAUJHLXrEnctn+/DJieeMKtetzcnLifEblmUHbggBt6B3gHg5MmSchvEEbkmlyyH/5QlhUVXo/JtGkSst3eLvcZW8QDXpEb5/SA6mq3gjQhmaapSaaymT076pb0HYWF4rBKp8gdOxZ44IHE7UOGyL22szO5yA3z5DY2SorT5s0iWG3v+oUXdt8uk8M7MqBWUo88uYf/Bux5TrykQfma+SUSsuz35CoF1F4AbF0GdDkVjtfOA47vAYZ9AqgKCNsxDDpLCksNqAvfB5DiVngeqHWqdZWOBLpOAgfWi8CtOdsV6L0Vq/3Jk6udXKcRM6NtByHZjn2DjkrkNjZKCGFURTVsT24OwXDliBg4EHjpJckVve46KURiRC7gVvz009rqejnr6rwVjA2rVyeGLJq83I0bxTMwcGBiYSlARK5SUvUTcIvZmEqffo9HGEOHenNU33xTPMiTJ3s9udOnS97sypWybjxRl18uSzOQUyreVWOXLAkeLBOSCW67Dbj//iTCJwdQKng6r76gtlaiVLROnHoI6F7kTpokAnnlytRmgygvl4ecdQF6sVtPbvEQyaH9+++AEZcGh7Iab6zfkwsAZ/9QQo+LaoBxNwE7fwF0HOk+73PINOCS1d6pe4KonCjL2hnOF3K+5Dv/I8sPfMvdN1VPbn8oPFU5HjjvcWDaw1G3hJDsJg4id+FCd0L0KLBzcnMIenIjpLISePZZeXjz9a9L+LGZP/ett4Bzz038TGur6+Wsr5e82pMnXeHY1gZs2CChyCtWuAVUTAGozZtl0FVb6045Yti1C1i+XDy3kyfL4O6VV0R49lbkFhTI99mzB7jkEink9OCDiQPUhgYZg5l8u7PP9ha7Mp7cqqrwwk5xwK6GSkimOffc4PtFrpFJkWv4+MfD3w8TuaZ2yKFDPb9n+rn//uCaARMnAmec4T6ITKCkFjjplO4ffpn3vY+uAKCBXf8HHPxLcP7sgJHAxU7OS+d7wJYfyevSEYn7psLYLwCVTUDlBPd8ALDtIRHAw60qXCmHK+fWQC2UUdmfM0dI5Cglf1pHU1kZkM4tqABEpshRTy5FbsQUFQFf+5oIyTfekDkdly0L9+S2tLgDrNGjJdR382bg5z8HXntNBoCdncCttwK//714Sp980hW5b78toY0DB7q5u4a5c6WC8jPPiKCcNs3dZ8cOecA1eDB6jJlGaNkyV/QC3odm3dXLMN81zqHKhJDMkCmRa3tJTYE9myuvlNoJZ54Z/PkJE+Qe2tWVmicXAGbNCm/bhg1JPmi8tCOvBOo+6X2v/l9kqfIAfVIKQQVR5XhbdZd4e0+0pq+Cb0EZMPwSd73UclePvcHbKVDkEkIyQUGBeIxydXqC7mBOLukr5swBXnxRntrPnw+8/LK3aJRNa6ubl26Kk9xyC/D8826O7u23A+ecI1MIDRsG/PrXEq6stQjij31MwuGWL5dret48EcYvvQQsXgx81KkHMn068I1viDdi+3bX69pTRo8Gdu9OzCtTCrjmmp4VbjOe3DgXnSKEZIZMiVxTD6GmJvieV1EhDyTDKCkBxo+Xh4qpityUGX0VUDcz+Xyyoz4rf92h8oChFwHNv+q7aWpKrMILTc7UAmWjgWM7gYJehg6W9KNwZUJI+sjPlwFxxm/YMYHhyqSvKCgQb6fh/POBpUuBn/1MxKBNS4vrTTUid+VK8c6+8YaEBZvwurPOkqUJWX73XSmO1NQkkQmdnVIQ6mEnpaemRgSvYcYMEcYLFgDvvNP7sLu77nILSfl55JGeHcN4VOjJJYSUlmZG5J52mjzkW7Qo9WNMniwiN9Vw5ZRRKrnA7S2jrwL+sVKm/ukL8vKBaT8DBp0J5DtVCS9ZCxx6XUR2bygZKtWGBwRU7CKEkDDanbnDJ0yIth1RwXBlkikWLRJP7vXXy/yyY8cCF10kntmjR93Km0bkdnRISF1xMXDppYnHGzJEPLmbN8t6U5MbMjx/vniQ77xT9rMjNWbMkLDne+6R9fPP7933GD9e/k6FwkLxqlDkEkIuvtg7V3dfMWCA1CM4FaZMAR5/PAccA6M+K6HPfTkX65jPe9dLhwKllwTvm4yCMuDTb3u9w4QQ0lOamqJuQTQwXJlkipISyaO9+GLgpptk24IFUojk9NOBG2+UbaWlrpf2jDPCj1dbKyL39ddlvanJW+F4yRJgZsAsBEoB3/0ucMEFUjk4LEesr5kzR743IaR/s3hx1C3oOTfdJF7c4X0U5ZtR+lLgpht6cQkhqUJPbrTtSDNZI3KVUpcCuA9APoAHtdYLI25Sn1JeLoWjVq+WyuILF0ohpyee8Hpb6+u7F7lnnAHcdx+wZYuEMI8cKWOWrVvFK9JdQbeZM4NFcKZYujS6cxNCSCoMHw584QtRt4IQQkiPCSpp3x/I0ZzcGE/K4qKUygfw3wAuAzAJwBylVJJZ6XODsjLx5j7yCHDDDcCqVYmhbyZkOagCqOGOO2TAtX+/zElrHso3NkZbsZwQQgghhJBYUBhScT7XYbhypJwNYIvWehsAKKV+CeAKAH+LtFUZYsQImWM2iKlTgTffTJ6nVl0NPP00sH49cN55fdNGQgghhBBCSJbR2Che3JzIr3FRWuuo29AtSqlZAC7VWs9z1ucCOEdr/WXffl8E8EUAqK+v//DOnTsz3tZM09UlVZL768MnQgghhBBCUmbxYmDixODqrf0BrUVMZGL6gj5AKfWa1nqqf3u2fJugqhcJ6lxr/QCABwBg6tSp8VfvaSAvT/4IIYQQQgghveSrX426BdGiVNYK3GRkizzaDWCUtT4SwJ6I2kIIIYQQQgghJKZki8hdB2C8UmqMUqoIwGwAT0XcJkIIIYQQQgghMSMrfNNa6w6l1JcBPA+ZQujHWuuNETeLEEIIIYQQQkjMyAqRCwBa62cBPBt1OwghhBBCCCGExJdsCVcmhBBCCCGEEEK6hSKXEEIIIYQQQkjOQJFLCCGEEEIIISRnoMglhBBCCCGEEJIzUOQSQgghhBBCCMkZKHIJIYQQQgghhOQMFLmEEEIIIYQQQnIGilxCCCGEEEIIITkDRS4hhBBCCCGEkJxBaa2jbkOfoJTaD2Bn1O1IwmAALVE3gvwT2iOe0C7xhHaJJ7RL/KBN4gdtEl9om3gSd7uM1loP8W/MWZEbd5RSf9ZaT426HUSgPeIJ7RJPaJd4QrvED9okftAm8YW2iSfZaheGKxNCCCGEEEIIyRkocgkhhBBCCCGE5AwUudHxQNQNIB5oj3hCu8QT2iWe0C7xgzaJH7RJfKFt4klW2oU5uYQQQgghhBBCcgZ6cgkhhBBCCCGE5AwUuT1EKTVKKfWCUmqTUmqjUmq+s32QUup3Sql3nOVAZ3uNs/9RpdRS37G+rZTapZQ62s05P6yU+qtSaotS6gdKKeVsn6GUWq+U6lBKzeqr7xxn4mQP6/1ZSimtlMq6CnTpIE42UUp9Xym1wfnbrJQ61FffOxuIyDaB+ymlipVSyx2brVVKNaT322YP6bKLUmqAUuoZpdRbznEWJjkn+5UkxMkm1vvsW2JiE/YtXiKyDfuWbkiXXZz3fqOUet05zo+UUvkh54xf36K15l8P/gAMB3Cm87oCwGYAkwAsArDA2b4AwN3O6zIAHwVwE4ClvmN9xDne0W7O+SqAaQAUgOcAXOZsbwBwOoCHAcyK+rfp7/aw2vAHAH8CMDXq34c28ezzbwB+HPXv0w9tE7gfgJsB/Mh5PRvA8qh/n2y3C4ABAC5wXhcB+GPQteC8z34lS2xitYF9S4xsYu3DviUa27BvyZBdnPcqnaUC8BiA2SHnjF3fQk9uD9Fa79Var3deHwGwCUAdgCsA/NTZ7acAPuPsc0xr/TKA9oBj/UlrvTfZ+ZRSwyH/WGu0/Jc8bB17h9b6DQBdaflyWUic7OHwn5CbR8Lx+wsxtIlhDoBHU/tWuUGmbdPNfvY5VwC4yO+56i+kyy5a6+Na6xec1+8DWA9gpP987Fe6J042cWDfEj+bGNi3ZNg2zvvsW7ohzX1+m/OyAPIAIqGYU1z7ForcFHBCID4EYC2AoeZic5a1aTpNHYDd1vpuZxvxEbU9lFIfAjBKa/3rNJ0r64naJlY7RgMYA2BVms6Z9WTINsmoA7DLOWcHgMMAajJw3liTLrsopaoBfBrAyoC32a/0gqhtwr4lkahtYn2efYuPDNkmGexbAkiHXZRSzwPYB+AI5AGCn1j2LRS5vUQpVQ5x13/VerrRJ6cK2MZS2D6itodSKg/A9wHc0ofnziqitolvfTaAFVrrzj5sR9aQQdskbUbAtn59b0uXXZRSBRDP0g+01tuCdgnY1q9/+zCitgn7lkSitolvnX2LRQZtk/TjAdv69f0tXXbRWn8CEgJdDODCoFMFfSzV86ULitxeoJQqhPyz/Fxr/biz+R+Om9646/eleOx8q5jBnZCnIHaoxkgAe1Jvfe4RE3tUAJgC4EWl1A5IrshTqv8WCImDTWxmo5+HkxkybJtk7AYwyvlcAYAqAAdSOW8ukGa7PADgHa31Yuez7FdSICY2Yd9iEROb2LBvcciwbZLBvsUi3X2+1rodwFMArsiWvqUg6gZkC05c/zIAm7TW37PeegrAdQAWOssnUzm+8zTwg75zHlFKfQQSYnAtgCWpHDsXiYs9tNaHAQy29nkRwK1a6z+nct5sJi42sd6bAGAggDWpnC+XiMI2STDnXANgFoBVTg5PvyOddlFK/RdkUDfPbGO/0nviYhP2LS5xsYn1HvsWhyhskwT2LQ7psovjCa7QWu91HhzMBPDHrOlbdAyqgGXDH6TqmAbwBoANzt9MSLz/SgDvOMtB1md2QJ4iHYU85ZjkbF/krHc5y2+GnHMqgDcBbAWwFIBytp/lfO4YgFYAG6P+ffqzPXz7vIj+WwEzVjYB8E0AC6P+XeLwF5FtAvcDUALgfwFsgVRjbIz698l2u0CemmtIcRFznHkh52S/kiU28e3zIti3xMImYN8StW3Yt2TOLkMBrHOOsxEiXAtCzhm7vsU0gBBCCCGEEEIIyXqYk0sIIYQQQgghJGegyCWEEEIIIYQQkjNQ5BJCCCGEEEIIyRkocgkhhBBCCCGE5AwUuYQQQgghhBBCcgaKXEIIIYQQQgghOQNFLiGEEEIIIYSQnIEilxBCCCGEEEJIzvD/N7RPUqPZhK0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### CASUAL COUNT PLOT ###\n",
    "\n",
    "plt.figure(figsize=(16,6))\n",
    "\n",
    "plt.plot(train_date, X_train.casual, label='train', color='blue')\n",
    "plt.plot(val_date, X_val.casual, label='val', color='orange')\n",
    "plt.plot(test_date, X_test.casual, label='test', color='red')\n",
    "plt.ylabel('casual cnt')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1cdd5076438>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### CASUAL COUNT DISTRIBUTION ###\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "plt.hist(X_train.casual, label='train', bins=25, alpha=0.5, color='blue')\n",
    "plt.hist(X_val.casual, label='val', bins=25, alpha=0.5, color='orange')\n",
    "plt.hist(X_test.casual, label='test', bins=25, alpha=0.5, color='red')\n",
    "plt.ylabel('frequency'); plt.xlabel('casual cnt')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "### DEFINE MAX PLAUSIBLE CASUAL COUNT ###\n",
    "max_count = 4000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1cdd51813c8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### CDF CASUAL COUNT PLOT ###\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "plt.hist(X_train.casual, range=(0,max_count), bins=30, \n",
    "         cumulative=True, density=True, alpha=0.5, color='blue')\n",
    "cdf = np.histogram(X_train.casual, range=(0,max_count), bins=max_count, density=True)[0].cumsum()\n",
    "plt.plot(cdf, c='blue', linewidth=3, label='train')\n",
    "\n",
    "plt.hist(X_val.casual, range=(0,max_count), bins=30, \n",
    "         cumulative=True, density=True, alpha=0.4, color='orange')\n",
    "cdf = np.histogram(X_val.casual, range=(0,max_count), bins=max_count, density=True)[0].cumsum()\n",
    "plt.plot(cdf, c='orange', linewidth=3, label='val')\n",
    "\n",
    "plt.hist(X_test.casual, range=(0,max_count), bins=30, \n",
    "         cumulative=True, density=True, alpha=0.3, color='red')\n",
    "cdf = np.histogram(X_test.casual, range=(0,max_count), bins=max_count, density=True)[0].cumsum()\n",
    "plt.plot(cdf, c='red', linewidth=3, label='test')\n",
    "\n",
    "plt.ylabel('cdf'); plt.xlabel('casual cnt')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SURVIVAL LGBM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(247252, 12)\n"
     ]
    }
   ],
   "source": [
    "### EXPAND TRAIN DATA ###\n",
    "\n",
    "X_train['count_so_far'] = X_train.apply(lambda x: np.arange(x.casual), axis=1)\n",
    "X_train['stop'] = X_train.apply(lambda x: np.append(np.zeros(x.casual-1), 1), axis=1)\n",
    "X_train = X_train.apply(pd.Series.explode)\n",
    "\n",
    "X_train['count_so_far'] = X_train.count_so_far.astype(int)\n",
    "X_train['stop'] = X_train.stop.astype(int)\n",
    "\n",
    "y_train = X_train['stop']\n",
    "X_train = X_train[columns+['count_so_far']]\n",
    "\n",
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(87764, 12)\n"
     ]
    }
   ],
   "source": [
    "### EXPAND VALID DATA ###\n",
    "\n",
    "X_val['count_so_far'] = X_val.apply(lambda x: np.arange(x.casual), axis=1)\n",
    "X_val['stop'] = X_val.apply(lambda x: np.append(np.zeros(x.casual-1), 1), axis=1)\n",
    "X_val = X_val.apply(pd.Series.explode)\n",
    "\n",
    "X_val['count_so_far'] = X_val.count_so_far.astype(int)\n",
    "X_val['stop'] = X_val.stop.astype(int)\n",
    "\n",
    "y_val = X_val['stop']\n",
    "X_val = X_val[columns+['count_so_far']]\n",
    "\n",
    "print(X_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(484000, 12)\n"
     ]
    }
   ],
   "source": [
    "### EXPAND VALID DATA FOR PREDICTION ###\n",
    "\n",
    "X_val_surv = df[(df.dteday >= datetime(year=2012, month=1, day=1))&(df.dteday < datetime(year=2012, month=5, day=1))].copy()\n",
    "\n",
    "X_val_surv['count_so_far'] = X_val_surv.apply(lambda x: np.arange(max_count), axis=1)\n",
    "X_val_surv['stop'] = X_val_surv.apply(lambda x:np.append(np.zeros(x.casual), np.ones(max_count-x.casual)), axis=1)\n",
    "X_val_surv = X_val_surv.apply(pd.Series.explode)\n",
    "\n",
    "X_val_surv['count_so_far'] = X_val_surv.count_so_far.astype(int)\n",
    "X_val_surv['stop'] = X_val_surv.stop.astype(int)\n",
    "\n",
    "y_val_surv = X_val_surv['stop']\n",
    "X_val_surv = X_val_surv[columns+['count_so_far']]\n",
    "\n",
    "print(X_val_surv.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(285001, 12)\n"
     ]
    }
   ],
   "source": [
    "### EXPAND TEST DATA ###\n",
    "\n",
    "X_test['count_so_far'] = X_test.apply(lambda x: np.arange(x.casual), axis=1)\n",
    "X_test['stop'] = X_test.apply(lambda x: np.append(np.zeros(x.casual-1), 1), axis=1)\n",
    "X_test = X_test.apply(pd.Series.explode)\n",
    "\n",
    "X_test['count_so_far'] = X_test.count_so_far.astype(int)\n",
    "X_test['stop'] = X_test.stop.astype(int)\n",
    "\n",
    "y_test = X_test['stop']\n",
    "X_test = X_test[columns+['count_so_far']]\n",
    "\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(980000, 12)\n"
     ]
    }
   ],
   "source": [
    "### EXPAND TEST DATA FOR PREDICTION ###\n",
    "\n",
    "X_test_surv = df[(df.dteday >= datetime(year=2012, month=5, day=1))].copy()\n",
    "\n",
    "X_test_surv['count_so_far'] = X_test_surv.apply(lambda x: np.arange(max_count), axis=1)\n",
    "X_test_surv['stop'] = X_test_surv.apply(lambda x: np.append(np.zeros(x.casual), np.ones(max_count-x.casual)), axis=1)\n",
    "X_test_surv = X_test_surv.apply(pd.Series.explode)\n",
    "\n",
    "X_test_surv['count_so_far'] = X_test_surv.count_so_far.astype(int)\n",
    "X_test_surv['stop'] = X_test_surv.stop.astype(int)\n",
    "\n",
    "y_test_surv = X_test_surv['stop']\n",
    "X_test_surv = X_test_surv[columns+['count_so_far']]\n",
    "\n",
    "print(X_test_surv.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1cdd66a0ba8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PLOT COUNT SO FAR VARIABLE ### \n",
    "\n",
    "plt.figure(figsize=(16,6))\n",
    "\n",
    "plt.plot(range(len(X_train)), X_train.count_so_far, label='train', color='blue')\n",
    "plt.plot(range(len(X_train),len(X_val)+len(X_train)), X_val.count_so_far, label='val', color='orange')\n",
    "plt.plot(range(len(X_val)+len(X_train),len(X_val)+len(X_train)+len(X_test)), X_test.count_so_far, label='test', color='red')\n",
    "plt.ylabel('casual cnt'); plt.xlabel('count so far')\n",
    "plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1cdd5156550>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PLOT COUNT SO FAR VARIABLE ZOOM ### \n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "csf = plt.plot(range(6000,15000), X_train.count_so_far[6000:15000], color='blue')\n",
    "plt.ylabel('count so far')\n",
    "plt.twinx()\n",
    "scr = plt.plot(range(6000,15000), y_train[6000:15000], color='magenta', alpha=0.6)\n",
    "plt.ylabel('stop')\n",
    "plt.legend(csf+scr, ['count so far', 'stop'], loc='upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "         'objective':'poisson',\n",
    "         'num_leaves':30, \n",
    "         'learning_rate': 0.001,\n",
    "         'feature_fraction': 0.8,\n",
    "         'bagging_fraction': 0.9,\n",
    "         'bagging_seed': 33,\n",
    "         'poisson_max_delta_step': 0.8,\n",
    "         'metric': 'poisson'\n",
    "         }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 150 rounds.\n",
      "[50]\ttraining's poisson: 0.0109487\tvalid_1's poisson: 0.0103453\n",
      "[100]\ttraining's poisson: 0.0108272\tvalid_1's poisson: 0.010255\n",
      "[150]\ttraining's poisson: 0.0107241\tvalid_1's poisson: 0.0102008\n",
      "[200]\ttraining's poisson: 0.0106252\tvalid_1's poisson: 0.0101618\n",
      "[250]\ttraining's poisson: 0.0105319\tvalid_1's poisson: 0.0101535\n",
      "[300]\ttraining's poisson: 0.010446\tvalid_1's poisson: 0.0101581\n",
      "[350]\ttraining's poisson: 0.0103616\tvalid_1's poisson: 0.0101667\n",
      "Early stopping, best iteration is:\n",
      "[247]\ttraining's poisson: 0.0105386\tvalid_1's poisson: 0.0101518\n"
     ]
    }
   ],
   "source": [
    "### FIT LGBM WITH POISSON LOSS ### \n",
    "\n",
    "trn_data = lgb.Dataset(X_train, label=y_train)\n",
    "val_data = lgb.Dataset(X_val, label=y_val)\n",
    "\n",
    "model = lgb.train(params, trn_data, num_boost_round=1000,\n",
    "                  valid_sets = [trn_data, val_data],\n",
    "                  verbose_eval=50, early_stopping_rounds=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(121, 4000)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### PREDICT HAZARD FUNCTION ON VALIDATION DATA AND TRANSFORM TO SURVIVAL ###\n",
    "\n",
    "p_val_hz = model.predict(X_val_surv).reshape(-1,max_count)\n",
    "p_val = 1-np.exp(-np.cumsum(p_val_hz, axis=1))\n",
    "p_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(121, 4000)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### GET TRUE CDF ON VALIDATION DATA ###\n",
    "\n",
    "t_val = y_val_surv.values.reshape(-1,max_count)\n",
    "t_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17432894983162234"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### CRPS ON VALIDATION DATA ###\n",
    "\n",
    "crps(t_val, p_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17963328860908506"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### CRPS ON VALIDATION DATA WITH BASELINE MODEL ###\n",
    "\n",
    "crps(t_val, np.repeat(cdf, len(t_val)).reshape(-1,max_count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(245, 4000)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### PREDICT HAZARD FUNCTION ON TEST DATA AND TRANSFORM TO SURVIVAL ###\n",
    "\n",
    "p_test_hz = model.predict(X_test_surv).reshape(-1,max_count)\n",
    "p_test = 1-np.exp(-np.cumsum(p_test_hz, axis=1))\n",
    "p_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(245, 4000)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### GET TRUE CDF ON TEST DATA ###\n",
    "\n",
    "t_test = y_test_surv.values.reshape(-1,max_count)\n",
    "t_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.28359956405330844"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### CRPS ON TEST DATA ###\n",
    "\n",
    "crps(t_test, p_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2892390593715204"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### CRPS ON TEST DATA WITH BASELINE MODEL ###\n",
    "\n",
    "crps(t_test, np.repeat(cdf, len(t_test)).reshape(-1,max_count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1cdd58e9438>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PLOT PREDICTIONS ###\n",
    "_id = 22\n",
    "\n",
    "plt.figure(figsize=(7,5))\n",
    "pred_surv = plt.plot(p_test[_id])\n",
    "true_cdf = plt.plot(t_test[_id], c='green')\n",
    "plt.ylabel('probability'); plt.xlabel('casual count')\n",
    "plt.twinx()\n",
    "pred_haz = plt.plot(p_test_hz[_id], c='orange')\n",
    "plt.title(pd.to_datetime(test_date[_id]))\n",
    "plt.ylabel('hazard')\n",
    "plt.legend(pred_surv+true_cdf+pred_haz, \n",
    "           ['pred surv f', 'true cdf', 'hazard f'], loc='lower right')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
